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Li W, Zhang Z, Xie B, He Y, He K, Qiu H, Lu Z, Jiang C, Pan X, He Y, Hu W, Liu W, Que T, Hu Y. HiOmics: A cloud-based one-stop platform for the comprehensive analysis of large-scale omics data. Comput Struct Biotechnol J 2024; 23:659-668. [PMID: 38292471 PMCID: PMC10824657 DOI: 10.1016/j.csbj.2024.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 01/01/2024] [Accepted: 01/02/2024] [Indexed: 02/01/2024] Open
Abstract
Analyzing the vast amount of omics data generated comprehensively by high-throughput sequencing technology is of utmost importance for scientists. In this context, we propose HiOmics, a cloud-based platform equipped with nearly 300 plugins designed for the comprehensive analysis and visualization of omics data. HiOmics utilizes the Element Plus framework to craft a user-friendly interface and harnesses Docker container technology to ensure the reliability and reproducibility of data analysis results. Furthermore, HiOmics employs the Workflow Description Language and Cromwell engine to construct workflows, ensuring the portability of data analysis and simplifying the examination of intricate data. Additionally, HiOmics has developed DataCheck, a tool based on Golang, which verifies and converts data formats. Finally, by leveraging the object storage technology and batch computing capabilities of public cloud platforms, HiOmics enables the storage and processing of large-scale data while maintaining resource independence among users.
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Affiliation(s)
- Wen Li
- Life Sciences Institute, Guangxi Medical University, Nanning, Guangxi, China
- Department of Biochemistry and Molecular Biology, School of Basic Medicine, Guangxi Medical University, Nanning, Guangxi, China
- Key Laboratory of Biological Molecular Medicine Research (Guangxi Medical University), Education Department of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
| | - Zhining Zhang
- Guangxi Henbio Biotechnology Co., Ltd., Nanning, Guangxi, China
| | - Bo Xie
- Life Sciences Institute, Guangxi Medical University, Nanning, Guangxi, China
| | - Yunlin He
- Guangxi Henbio Biotechnology Co., Ltd., Nanning, Guangxi, China
| | - Kangming He
- Guangxi Henbio Biotechnology Co., Ltd., Nanning, Guangxi, China
| | - Hong Qiu
- Life Sciences Institute, Guangxi Medical University, Nanning, Guangxi, China
- Guangxi Henbio Biotechnology Co., Ltd., Nanning, Guangxi, China
| | - Zhiwei Lu
- Guangxi Henbio Biotechnology Co., Ltd., Nanning, Guangxi, China
| | - Chunlan Jiang
- Guangxi Henbio Biotechnology Co., Ltd., Nanning, Guangxi, China
| | - Xuanyu Pan
- School of Basic Medicine, Guangxi Medical University, Nanning, Guangxi, China
| | - Yuxiao He
- Life Sciences Institute, Guangxi Medical University, Nanning, Guangxi, China
| | - Wenyu Hu
- Guangxi Henbio Biotechnology Co., Ltd., Nanning, Guangxi, China
| | - Wenjian Liu
- Faculty of Data Science, City University of Macau, Macau, China
| | - Tengcheng Que
- Faculty of Data Science, City University of Macau, Macau, China
- Youjiang Medical University for Nationalities, Baise, Guangxi, China
- Guangxi Zhuang Autonomous Terrestrial Wildlife Rescue Research and Epidemic Diseases Monitoring Center, Nanning, Guangxi, China
| | - Yanling Hu
- Life Sciences Institute, Guangxi Medical University, Nanning, Guangxi, China
- Department of Biochemistry and Molecular Biology, School of Basic Medicine, Guangxi Medical University, Nanning, Guangxi, China
- Key Laboratory of Biological Molecular Medicine Research (Guangxi Medical University), Education Department of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
- Guangxi Henbio Biotechnology Co., Ltd., Nanning, Guangxi, China
- Faculty of Data Science, City University of Macau, Macau, China
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Ma W, Tang W, Kwok JS, Tong AH, Lo CW, Chu AT, Chung BH. A review on trends in development and translation of omics signatures in cancer. Comput Struct Biotechnol J 2024; 23:954-971. [PMID: 38385061 PMCID: PMC10879706 DOI: 10.1016/j.csbj.2024.01.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 01/31/2024] [Accepted: 01/31/2024] [Indexed: 02/23/2024] Open
Abstract
The field of cancer genomics and transcriptomics has evolved from targeted profiling to swift sequencing of individual tumor genome and transcriptome. The steady growth in genome, epigenome, and transcriptome datasets on a genome-wide scale has significantly increased our capability in capturing signatures that represent both the intrinsic and extrinsic biological features of tumors. These biological differences can help in precise molecular subtyping of cancer, predicting tumor progression, metastatic potential, and resistance to therapeutic agents. In this review, we summarized the current development of genomic, methylomic, transcriptomic, proteomic and metabolic signatures in the field of cancer research and highlighted their potentials in clinical applications to improve diagnosis, prognosis, and treatment decision in cancer patients.
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Affiliation(s)
- Wei Ma
- Hong Kong Genome Institute, Hong Kong, China
| | - Wenshu Tang
- Hong Kong Genome Institute, Hong Kong, China
| | | | | | | | | | - Brian H.Y. Chung
- Hong Kong Genome Institute, Hong Kong, China
- Department of Pediatrics and Adolescent Medicine, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Hong Kong Genome Project
- Hong Kong Genome Institute, Hong Kong, China
- Department of Pediatrics and Adolescent Medicine, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
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Zhang ZW, Zhang KX, Liao X, Quan Y, Zhang HY. Evolutionary screening of precision oncology biomarkers and its applications in prognostic model construction. iScience 2024; 27:109859. [PMID: 38799582 PMCID: PMC11126775 DOI: 10.1016/j.isci.2024.109859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 03/15/2024] [Accepted: 04/27/2024] [Indexed: 05/29/2024] Open
Abstract
Biomarker screening is critical for precision oncology. However, one of the main challenges in precision oncology is that the screened biomarkers often fail to achieve the expected clinical effects and are rarely approved by regulatory authorities. Considering the close association between cancer pathogenesis and the evolutionary events of organisms, we first explored the evolutionary feature underlying clinically approved biomarkers, and two evolutionary features of approved biomarkers (Ohnologs and specific evolutionary stages of genes) were identified. Subsequently, we utilized evolutionary features for screening potential prognostic biomarkers in four common cancers: head and neck squamous cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, and lung squamous cell carcinoma. Finally, we constructed an evolution-strengthened prognostic model (ESPM) for cancers. These models can predict cancer patients' survival time across different cancer cohorts effectively and perform better than conventional models. In summary, our study highlights the application potentials of evolutionary information in precision oncology biomarker screening.
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Affiliation(s)
- Zhi-Wen Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Ke-Xin Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Xuan Liao
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Yuan Quan
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Hong-Yu Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China
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4
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Choudhury P, Dasgupta S, Bhattacharyya P, Roychowdhury S, Chaudhury K. Understanding pulmonary hypertension: the need for an integrative metabolomics and transcriptomics approach. Mol Omics 2024. [PMID: 38853716 DOI: 10.1039/d3mo00266g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Pulmonary hypertension (PH), characterised by mean pulmonary arterial pressure (mPAP) >20 mm Hg at rest, is a complex pathophysiological disorder associated with multiple clinical conditions. The high prevalence of the disease along with increased mortality and morbidity makes it a global health burden. Despite major advances in understanding the disease pathophysiology, much of the underlying complex molecular mechanism remains to be elucidated. Lack of a robust diagnostic test and specific therapeutic targets also poses major challenges. This review provides a comprehensive update on the dysregulated pathways and promising candidate markers identified in PH patients using the transcriptomics and metabolomics approach. The review also highlights the need of using an integrative multi-omics approach for obtaining insight into the disease at a molecular level. The integrative multi-omics/pan-omics approach envisaged to help in bridging the gap from genotype to phenotype is outlined. Finally, the challenges commonly encountered while conducting omics-driven studies are also discussed.
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Affiliation(s)
- Priyanka Choudhury
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, 721302, West Bengal, India.
| | - Sanjukta Dasgupta
- Department of Biotechnology, Brainware University, Barasat, West Bengal, India
| | | | | | - Koel Chaudhury
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, 721302, West Bengal, India.
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5
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Lockwood MB, Sung C, Alvernaz SA, Lee JR, Chin JL, Nayebpour M, Bernabé BP, Tussing-Humphreys LM, Li H, Spaggiari M, Martinino A, Park CG, Chlipala GE, Doorenbos AZ, Green SJ. The Gut Microbiome and Symptom Burden After Kidney Transplantation: An Overview and Research Opportunities. Biol Res Nurs 2024:10998004241256031. [PMID: 38836469 DOI: 10.1177/10998004241256031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2024]
Abstract
Many kidney transplant recipients continue to experience high symptom burden despite restoration of kidney function. High symptom burden is a significant driver of quality of life. In the post-transplant setting, high symptom burden has been linked to negative outcomes including medication non-adherence, allograft rejection, graft loss, and even mortality. Symbiotic bacteria (microbiota) in the human gastrointestinal tract critically interact with the immune, endocrine, and neurological systems to maintain homeostasis of the host. The gut microbiome has been proposed as an underlying mechanism mediating symptoms in several chronic medical conditions including irritable bowel syndrome, chronic fatigue syndrome, fibromyalgia, and psychoneurological disorders via the gut-brain-microbiota axis, a bidirectional signaling pathway between the enteric and central nervous system. Post-transplant exposure to antibiotics, antivirals, and immunosuppressant medications results in significant alterations in gut microbiota community composition and function, which in turn alter these commensal microorganisms' protective effects. This overview will discuss the current state of the science on the effects of the gut microbiome on symptom burden in kidney transplantation and future directions to guide this field of study.
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Affiliation(s)
- Mark B Lockwood
- Department of Biobehavioral Nursing Science, University of Illinois Chicago College of Nursing, Chicago, IL, USA
| | - Choa Sung
- Post-Doctoral Fellow, Department of Biobehavioral Nursing Science, University of Illinois Chicago College of Nursing, Chicago, IL, USA
| | - Suzanne A Alvernaz
- Graduate Student, Department of Biomedical Engineering, University of Illinois ChicagoColleges of Engineering and Medicine, Chicago, IL, USA
| | - John R Lee
- Division of Nephrology and Hypertension, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Jennifer L Chin
- Medical Student, Touro College of Osteopathic Medicine, Middletown, NY, USA
| | - Mehdi Nayebpour
- Virginia BioAnalytics LLC, Washington, District of Columbia, USA
| | - Beatriz Peñalver Bernabé
- Graduate Student, Department of Biomedical Engineering, University of Illinois ChicagoColleges of Engineering and Medicine, Chicago, IL, USA
| | - Lisa M Tussing-Humphreys
- Department of Kinesiology and Nutrition, College of Applied Health Sciences, University of Illinois Chicago, Chicago, IL, USA
| | - Hongjin Li
- Department of Biobehavioral Nursing Science, University of Illinois Chicago College of Nursing, Chicago, IL, USA
| | - Mario Spaggiari
- Division of Transplantation, Department of Surgery, University of Illinois at Chicago, Chicago, IL, USA
| | - Alessandro Martinino
- Division of Transplantation, Department of Surgery, University of Illinois at Chicago, Chicago, IL, USA
| | - Chang G Park
- Department of Population Health Nursing Science, Office of Research Facilitation, University of Illinois Chicago, Chicago, IL, USA
| | - George E Chlipala
- Research Core Facility, Research Resources Center, University of Illinois Chicago, Chicago, IL, USA
| | - Ardith Z Doorenbos
- Department of Biobehavioral Nursing Science, University of Illinois ChicagoCollege of Nursing, Chicago, IL, USA
| | - Stefan J Green
- Department of Internal Medicine, Division of Infectious Diseases, Rush University Medical Center, Chicago, IL, USA
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Resende RT, Hickey L, Amaral CH, Peixoto LL, Marcatti GE, Xu Y. Satellite-enabled enviromics to enhance crop improvement. MOLECULAR PLANT 2024; 17:848-866. [PMID: 38637991 DOI: 10.1016/j.molp.2024.04.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 04/04/2024] [Accepted: 04/11/2024] [Indexed: 04/20/2024]
Abstract
Enviromics refers to the characterization of micro- and macroenvironments based on large-scale environmental datasets. By providing genotypic recommendations with predictive extrapolation at a site-specific level, enviromics could inform plant breeding decisions across varying conditions and anticipate productivity in a changing climate. Enviromics-based integration of statistics, envirotyping (i.e., determining environmental factors), and remote sensing could help unravel the complex interplay of genetics, environment, and management. To support this goal, exhaustive envirotyping to generate precise environmental profiles would significantly improve predictions of genotype performance and genetic gain in crops. Already, informatics management platforms aggregate diverse environmental datasets obtained using optical, thermal, radar, and light detection and ranging (LiDAR)sensors that capture detailed information about vegetation, surface structure, and terrain. This wealth of information, coupled with freely available climate data, fuels innovative enviromics research. While enviromics holds immense potential for breeding, a few obstacles remain, such as the need for (1) integrative methodologies to systematically collect field data to scale and expand observations across the landscape with satellite data; (2) state-of-the-art AI models for data integration, simulation, and prediction; (3) cyberinfrastructure for processing big data across scales and providing seamless interfaces to deliver forecasts to stakeholders; and (4) collaboration and data sharing among farmers, breeders, physiologists, geoinformatics experts, and programmers across research institutions. Overcoming these challenges is essential for leveraging the full potential of big data captured by satellites to transform 21st century agriculture and crop improvement through enviromics.
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Affiliation(s)
- Rafael T Resende
- Universidade Federal de Goiás (UFG), Agronomy Department, Plant Breeding Sector, Goiânia (GO) 74690-900, Brazil; TheCROP, a Precision-Breeding Startup: Enviromics, Phenomics, and Genomics, No Zip-code, Operating Virtually, Goiânia (GO) and Sete Lagoas (MG), Brazil.
| | - Lee Hickey
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, Australia
| | - Cibele H Amaral
- Earth Lab, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO 80303, USA; Environmental Data Science Innovation & Inclusion Lab, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO 80303, USA
| | - Lucas L Peixoto
- Universidade Federal de Goiás (UFG), Agronomy Department, Plant Breeding Sector, Goiânia (GO) 74690-900, Brazil
| | - Gustavo E Marcatti
- TheCROP, a Precision-Breeding Startup: Enviromics, Phenomics, and Genomics, No Zip-code, Operating Virtually, Goiânia (GO) and Sete Lagoas (MG), Brazil; Universidade Federal de São João del-Rei, Forest Engineering Department, Campus Sete Lagoas, Sete Lagoas (MG) 35701-970, Brazil
| | - Yunbi Xu
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; Peking University Institute of Advanced Agricultural Sciences, Weifang, Shandong 261325, China; BGI Bioverse, Shenzhen 518083, China.
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Wang R, He Z, Chen H, Guo S, Zhang S, Wang K, Wang M, Ho SH. Enhancing biomass conversion to bioenergy with machine learning: Gains and problems. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 927:172310. [PMID: 38599406 DOI: 10.1016/j.scitotenv.2024.172310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 03/20/2024] [Accepted: 04/06/2024] [Indexed: 04/12/2024]
Abstract
The growing concerns about environmental sustainability and energy security, such as exhaustion of traditional fossil fuels and global carbon footprint growth have led to an increasing interest in alternative energy sources, especially bioenergy. Recently, numerous scenarios have been proposed regarding the use of bioenergy from different sources in the future energy systems. In this regard, one of the biggest challenges for scientists is managing, modeling, decision-making, and future forecasting of bioenergy systems. The development of machine learning (ML) techniques can provide new opportunities for modeling, optimizing and managing the production, consumption and environmental effects of bioenergy. However, researchers in bioenergy fields have not widely utilized the ML concepts and practices. Therefore, a comparative review of the current ML techniques used for bioenergy productions is presented in this paper. This review summarizes the common issues and difficulties existing in integrating ML with bioenergy studies, and discusses and proposes the possible solutions. Additionally, a detailed discussion of the appropriate ML application scenarios is also conducted in every sector of the entire bioenergy chain. This indicates the modernized conversion processes supported by ML techniques are imperative to accurately capture process-level subtleties, and thus improving techno-economic resilience and socio-ecological integrity of bioenergy production. All the efforts are believed to help in sustainable bioenergy production with ML technologies for the future.
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Affiliation(s)
- Rupeng Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China
| | - Zixiang He
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China
| | - Honglin Chen
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China
| | - Silin Guo
- School of Medicine and Health, Harbin Institute of Technology, Harbin 150040, PR China
| | - Shiyu Zhang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China
| | - Ke Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China
| | - Meng Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China
| | - Shih-Hsin Ho
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China.
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Xu Z, Li W, Dong X, Chen Y, Zhang D, Wang J, Zhou L, He G. Precision medicine in colorectal cancer: Leveraging multi-omics, spatial omics, and artificial intelligence. Clin Chim Acta 2024; 559:119686. [PMID: 38663471 DOI: 10.1016/j.cca.2024.119686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 04/22/2024] [Accepted: 04/22/2024] [Indexed: 05/03/2024]
Abstract
Colorectal cancer (CRC) is a leading cause of cancer-related deaths. Recent advancements in genomic technologies and analytical approaches have revolutionized CRC research, enabling precision medicine. This review highlights the integration of multi-omics, spatial omics, and artificial intelligence (AI) in advancing precision medicine for CRC. Multi-omics approaches have uncovered molecular mechanisms driving CRC progression, while spatial omics have provided insights into the spatial heterogeneity of gene expression in CRC tissues. AI techniques have been utilized to analyze complex datasets, identify new treatment targets, and enhance diagnosis and prognosis. Despite the tumor's heterogeneity and genetic and epigenetic complexity, the fusion of multi-omics, spatial omics, and AI shows the potential to overcome these challenges and advance precision medicine in CRC. The future lies in integrating these technologies to provide deeper insights and enable personalized therapies for CRC patients.
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Affiliation(s)
- Zishan Xu
- Department of Pathology, Xinxiang Medical University, Xinxiang 453000, China
| | - Wei Li
- School of Forensic Medicine, Xinxiang Medical University, Xinxiang 453000, China
| | - Xiangyang Dong
- Department of Pathology, Xinxiang Medical University, Xinxiang 453000, China
| | - Yingying Chen
- School of Basic Medical Sciences, Xinxiang Medical University, Xinxiang 453000, China
| | - Dan Zhang
- Department of Pathology, Xinxiang Medical University, Xinxiang 453000, China
| | - Jingnan Wang
- Xinxiang Medical University SanQuan Medical College, Xinxiang 453003, China
| | - Lin Zhou
- Department of Breast and Thyroid Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
| | - Guoyang He
- Department of Pathology, Xinxiang Medical University, Xinxiang 453000, China.
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Gómez-Pascual A, Naccache T, Xu J, Hooshmand K, Wretlind A, Gabrielli M, Lombardo MT, Shi L, Buckley NJ, Tijms BM, Vos SJB, Ten Kate M, Engelborghs S, Sleegers K, Frisoni GB, Wallin A, Lleó A, Popp J, Martinez-Lage P, Streffer J, Barkhof F, Zetterberg H, Visser PJ, Lovestone S, Bertram L, Nevado-Holgado AJ, Gualerzi A, Picciolini S, Proitsi P, Verderio C, Botía JA, Legido-Quigley C. Paired plasma lipidomics and proteomics analysis in the conversion from mild cognitive impairment to Alzheimer's disease. Comput Biol Med 2024; 176:108588. [PMID: 38761503 DOI: 10.1016/j.compbiomed.2024.108588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 05/09/2024] [Accepted: 05/09/2024] [Indexed: 05/20/2024]
Abstract
BACKGROUND Alzheimer's disease (AD) is a neurodegenerative condition for which there is currently no available medication that can stop its progression. Previous studies suggest that mild cognitive impairment (MCI) is a phase that precedes the disease. Therefore, a better understanding of the molecular mechanisms behind MCI conversion to AD is needed. METHOD Here, we propose a machine learning-based approach to detect the key metabolites and proteins involved in MCI progression to AD using data from the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery Study. Proteins and metabolites were evaluated separately in multiclass models (controls, MCI and AD) and together in MCI conversion models (MCI stable vs converter). Only features selected as relevant by 3/4 algorithms proposed were kept for downstream analysis. RESULTS Multiclass models of metabolites highlighted nine features further validated in an independent cohort (0.726 mean balanced accuracy). Among these features, one metabolite, oleamide, was selected by all the algorithms. Further in-vitro experiments in rodents showed that disease-associated microglia excreted oleamide in vesicles. Multiclass models of proteins stood out with nine features, validated in an independent cohort (0.720 mean balanced accuracy). However, none of the proteins was selected by all the algorithms. Besides, to distinguish between MCI stable and converters, 14 key features were selected (0.872 AUC), including tTau, alpha-synuclein (SNCA), junctophilin-3 (JPH3), properdin (CFP) and peptidase inhibitor 15 (PI15) among others. CONCLUSIONS This omics integration approach highlighted a set of molecules associated with MCI conversion important in neuronal and glia inflammation pathways.
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Affiliation(s)
- Alicia Gómez-Pascual
- Department of Information and Communications Engineering Faculty of Informatics, University of Murcia, Murcia, Spain; Steno Diabetes Center Copenhagen, Herlev, Denmark
| | - Talel Naccache
- Department of Data Science, City University of London, United Kingdom
| | - Jin Xu
- Institute of Pharmaceutical Science, King's College London, London, United Kingdom
| | | | | | | | - Marta Tiffany Lombardo
- CNR Institute of Neuroscience, 20854, Vedano al Lambro, Italy; School of Medicine and Surgery, University of Milano-Bicocca, 20126, Italy
| | - Liu Shi
- Novo Nordisk Research Centre Oxford (NNRCO), Oxford, United Kingdom
| | - Noel J Buckley
- Department of Psychiatry, University of Oxford, United Kingdom; Kavli Institute for Nanoscience Discovery, Denmark
| | - Betty M Tijms
- Alzheimer Center, VU University Medical Center, Amsterdam, the Netherlands
| | - Stephanie J B Vos
- Department of Psychiatry and Neuropsychology, Alzheimer Centrum Limburg, Maastricht University, Maastricht, the Netherlands
| | - Mara Ten Kate
- Alzheimer Center, VU University Medical Center, Amsterdam, the Netherlands
| | - Sebastiaan Engelborghs
- Reference Center for Biological Markers of Dementia (BIODEM), Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium; Department of Neurology and Bru-BRAIN, UZ Brussel and Center for Neurosciences (C4N), Vrije Universiteit Brussel, Brussels, Belgium
| | - Kristel Sleegers
- Complex Genetics Group, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium; Institute Born-Bunge, Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Giovanni B Frisoni
- University of Geneva, Geneva, Switzerland; IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Anders Wallin
- Institute of Neuroscience and Physiology, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Alberto Lleó
- Neurology Department, Hospital Sant Pau, Barcelona, Spain, Centro de Investigación en Red en enfermedades neurodegenerativas (CIBERNED)
| | - Julius Popp
- Old age psychiatry, University Hospital of Lausanne, University of Lausanne, Switzerland; Department of Geriatric Psychiatry, University Hospital of Psychiatry Zürich, University of Zürich, Switzerland
| | | | - Johannes Streffer
- AC Immune SA, Lausanne, Switzerland, formerly Janssen R&D, LLC. Beerse, Belgium at the time of study conduct
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit, the Netherlands; Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, United Kingdom
| | - Henrik Zetterberg
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden; Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden; UK Dementia Research Institute at UCL, London, United Kingdom; Department of Neurodegenerative Disease, UCL Institute of Neurology, London, United Kingdom
| | - Pieter Jelle Visser
- Alzheimer Center, VU University Medical Center, Amsterdam, the Netherlands; Department of Psychiatry and Neuropsychology, Alzheimer Centrum Limburg, Maastricht University, Maastricht, the Netherlands
| | - Simon Lovestone
- Department of Psychiatry, University of Oxford, United Kingdom; Janssen Medical (UK), High Wycombe, United Kingdom
| | - Lars Bertram
- Lübeck Interdisciplinary Platform for Genome Analytics, University of Lübeck, Lübeck, Germany; Department of Psychology, University of Oslo, Oslo, Norway
| | | | - Alice Gualerzi
- IRCCS Fondazione Don Carlo Gnocchi ONLUS in Milan, Italy
| | | | - Petroula Proitsi
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | | | - Juan A Botía
- Department of Information and Communications Engineering Faculty of Informatics, University of Murcia, Murcia, Spain
| | - Cristina Legido-Quigley
- Steno Diabetes Center Copenhagen, Herlev, Denmark; Institute of Pharmaceutical Science, King's College London, London, United Kingdom.
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Helmy M, Elhalis H, Rashid MM, Selvarajoo K. Can digital twin efforts shape microorganism-based alternative food? Curr Opin Biotechnol 2024; 87:103115. [PMID: 38547588 DOI: 10.1016/j.copbio.2024.103115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 03/05/2024] [Accepted: 03/06/2024] [Indexed: 06/09/2024]
Abstract
With the continuous increment in global population growth, compounded by post-pandemic food security challenges due to labor shortages, effects of climate change, political conflicts, limited land for agriculture, and carbon emissions control, addressing food production in a sustainable manner for future generations is critical. Microorganisms are potential alternative food sources that can help close the gap in food production. For the development of more efficient and yield-enhancing products, it is necessary to have a better understanding on the underlying regulatory molecular pathways of microbial growth. Nevertheless, as microbes are regulated at multiomics scales, current research focusing on single omics (genomics, proteomics, or metabolomics) independently is inadequate for optimizing growth and product output. Here, we discuss digital twin (DT) approaches that integrate systems biology and artificial intelligence in analyzing multiomics datasets to yield a microbial replica model for in silico testing before production. DT models can thus provide a holistic understanding of microbial growth, metabolite biosynthesis mechanisms, as well as identifying crucial production bottlenecks. Our argument, therefore, is to support the development of novel DT models that can potentially revolutionize microorganism-based alternative food production efficiency.
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Affiliation(s)
- Mohamed Helmy
- Vaccine and Infectious Disease Organization (VIDO), University of Saskatchewan, SK, Canada; Department of Computer Science, Lakehead University, ON, Canada; Department of Computer Science, College of Science and Engineering, Idaho State University, ID, USA; Bioinformatics Institute (BII), Agency for Science, Technology and Research (A⁎STAR), Singapore 138671, Singapore
| | - Hosam Elhalis
- Research School of Biology, Australian National University, Canberra, Australia
| | - Md Mamunur Rashid
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A⁎STAR), Singapore 138671, Singapore
| | - Kumar Selvarajoo
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A⁎STAR), Singapore 138671, Singapore; Synthetic Biology Translational Research Program and SynCTI, Yong Loo Lin School of Medicine, National University of Singapore (NUS), Singapore 117456, Singapore; School of Biological Sciences, Nanyang Technological University (NTU), Singapore 637551, Singapore.
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11
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Acharjee A, Okyere D, Nath D, Nagar S, Gkoutos GV. Network dynamics and therapeutic aspects of mRNA and protein markers with the recurrence sites of pancreatic cancer. Heliyon 2024; 10:e31437. [PMID: 38803850 PMCID: PMC11128524 DOI: 10.1016/j.heliyon.2024.e31437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 05/11/2024] [Accepted: 05/15/2024] [Indexed: 05/29/2024] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a deadly disease that typically manifests late patient presentation and poor outcomes. Furthermore, PDAC recurrence is a common challenge. Distinct patterns of PDAC recurrence have been associated with differential activation of immune pathway-related genes and specific inflammatory responses in their tumour microenvironment. However, the molecular associations between and within cellular components that underpin PDAC recurrence require further development, especially from a multi-omics integration perspective. In this study, we identified stable molecular associations across multiple PDAC recurrences and utilised integrative analytics to identify stable and novel associations via simultaneous feature selection. Spatial transcriptome and proteome datasets were used to perform univariate analysis, Spearman partial correlation analysis, and univariate analyses by Machine Learning methods, including regularised canonical correlation analysis and sparse partial least squares. Furthermore, networks were constructed for reported and new stable associations. Our findings revealed gene and protein associations across multiple PDAC recurrence groups, which can provide a better understanding of the multi-layer disease mechanisms that contribute to PDAC recurrence. These findings may help to provide novel association targets for clinical studies for constructing precision medicine and personalised surveillance tools for patients with PDAC recurrence.
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Affiliation(s)
- Animesh Acharjee
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom
- MRC Health Data Research UK (HDR UK), Birmingham, United Kingdom
- Institute of Translational Medicine, University Hospitals Birmingham NHS, Foundation Trust, B15 2TT, United Kingdom
- Centre for Health Data Research, University of Birmingham, B15 2TT, United Kingdom
| | - Daniella Okyere
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Dipanwita Nath
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Shruti Nagar
- Eureka Tutorials, Muzaffarnagar, U.P., 251201, India
| | - Georgios V. Gkoutos
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom
- MRC Health Data Research UK (HDR UK), Birmingham, United Kingdom
- Institute of Translational Medicine, University Hospitals Birmingham NHS, Foundation Trust, B15 2TT, United Kingdom
- Centre for Health Data Research, University of Birmingham, B15 2TT, United Kingdom
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12
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Kuraz Abebe B, Wang J, Guo J, Wang H, Li A, Zan L. A review of the role of epigenetic studies for intramuscular fat deposition in beef cattle. Gene 2024; 908:148295. [PMID: 38387707 DOI: 10.1016/j.gene.2024.148295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 01/23/2024] [Accepted: 02/15/2024] [Indexed: 02/24/2024]
Abstract
Intramuscular fat (IMF) deposition profoundly influences meat quality and economic value in beef cattle production. Meanwhile, contemporary developments in epigenetics have opened new outlooks for understanding the molecular basics of IMF regulation, and it has become a key area of research for world scholars. Therefore, the aim of this paper was to provide insight and synthesis into the intricate relationship between epigenetic mechanisms and IMF deposition in beef cattle. The methodology involves a thorough analysis of existing literature, including pertinent books, academic journals, and online resources, to provide a comprehensive overview of the role of epigenetic studies in IMF deposition in beef cattle. This review summarizes the contemporary studies in epigenetic mechanisms in IMF regulation, high-resolution epigenomic mapping, single-cell epigenomics, multi-omics integration, epigenome editing approaches, longitudinal studies in cattle growth, environmental epigenetics, machine learning in epigenetics, ethical and regulatory considerations, and translation to industry practices from perspectives of IMF deposition in beef cattle. Moreover, this paper highlights DNA methylation, histone modifications, acetylation, phosphorylation, ubiquitylation, non-coding RNAs, DNA hydroxymethylation, epigenetic readers, writers, and erasers, chromatin immunoprecipitation followed by sequencing, whole genome bisulfite sequencing, epigenome-wide association studies, and their profound impact on the expression of crucial genes governing adipogenesis and lipid metabolism. Nutrition and stress also have significant influences on epigenetic modifications and IMF deposition. The key findings underscore the pivotal role of epigenetic studies in understanding and enhancing IMF deposition in beef cattle, with implications for precision livestock farming and ethical livestock management. In conclusion, this review highlights the crucial significance of epigenetic pathways and environmental factors in affecting IMF deposition in beef cattle, providing insightful information for improving the economics and meat quality of cattle production.
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Affiliation(s)
- Belete Kuraz Abebe
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, People's Republic of China; Department of Animal Science, Werabe University, P.O. Box 46, Werabe, Ethiopia
| | - Jianfang Wang
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, People's Republic of China
| | - Juntao Guo
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, People's Republic of China
| | - Hongbao Wang
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, People's Republic of China
| | - Anning Li
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, People's Republic of China
| | - Linsen Zan
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, People's Republic of China; National Beef Cattle Improvement Center, Northwest A&F University, Yangling, Shaanxi 712100, People's Republic of China.
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13
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Yang H, Zhao L, Li D, An C, Fang X, Chen Y, Liu J, Xiao T, Wang Z. Subtype-WGME enables whole-genome-wide multi-omics cancer subtyping. CELL REPORTS METHODS 2024:100781. [PMID: 38761803 DOI: 10.1016/j.crmeth.2024.100781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 01/05/2024] [Accepted: 04/26/2024] [Indexed: 05/20/2024]
Abstract
We present an innovative strategy for integrating whole-genome-wide multi-omics data, which facilitates adaptive amalgamation by leveraging hidden layer features derived from high-dimensional omics data through a multi-task encoder. Empirical evaluations on eight benchmark cancer datasets substantiated that our proposed framework outstripped the comparative algorithms in cancer subtyping, delivering superior subtyping outcomes. Building upon these subtyping results, we establish a robust pipeline for identifying whole-genome-wide biomarkers, unearthing 195 significant biomarkers. Furthermore, we conduct an exhaustive analysis to assess the importance of each omic and non-coding region features at the whole-genome-wide level during cancer subtyping. Our investigation shows that both omics and non-coding region features substantially impact cancer development and survival prognosis. This study emphasizes the potential and practical implications of integrating genome-wide data in cancer research, demonstrating the potency of comprehensive genomic characterization. Additionally, our findings offer insightful perspectives for multi-omics analysis employing deep learning methodologies.
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Affiliation(s)
- Hai Yang
- Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Liang Zhao
- Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Dongdong Li
- Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Congcong An
- Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Xiaoyang Fang
- Cornell Tech, Cornell University, New York, NY 14853, USA
| | - Yiwen Chen
- Center for Continuing and Lifelong Education, National University of Singapore, Singapore 119077, Singapore
| | - Jingping Liu
- Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Ting Xiao
- Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Zhe Wang
- Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, China.
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14
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Esquivel Gaytan A, Bomer N, Grote Beverborg N, van der Meer P. 404-error "Disease not found": Unleashing the translational potential of -omics approaches beyond traditional disease classification in heart failure research. Eur J Heart Fail 2024. [PMID: 38741225 DOI: 10.1002/ejhf.3268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 03/15/2024] [Accepted: 04/14/2024] [Indexed: 05/16/2024] Open
Abstract
The emergence of personalized medicine, facilitated by the progress in -omics technologies, has initiated a new era in medical diagnostics and treatment. This review examines the potential of -omics approaches in heart failure, a condition that has not yet fully capitalized on personalized strategies compared to other medical fields like cancer therapy. Here, we argue that integrating multi-omics technology with systems medicine approaches could fundamentally transform heart failure management, moving away from the traditional paradigm of 'one size fits all'. Our review examines how omics can enhance understanding of heart failure's molecular foundations and contribute to a more comprehensive disease classification. We draw attention to the current state of medical practice that only relies on clinical evidence and a number of standard laboratory tests. At the same time, we propose a shift towards a universal approach that uses quantitative data from multi-omics to unravel complex molecular interactions. The discussion centres around the potential of the transition as a means to enhance individual risk assessment and emphasizes management within clinical settings. While the use of omics in cardiovascular research is not recent, many past studies have focused only on a single omics approach. In order to achieve a better understanding of disease mechanisms, we explore more holistic approaches using genomics, transcriptomics, epigenomics, and proteomics. This review concludes with a call to action to adopt multi-omics in clinical trials and practice to pave the way for more personalized disease management and more effective heart failure interventions.
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Affiliation(s)
- Antonio Esquivel Gaytan
- Department of Cardiology, University Medical Centre Groningen, University of Groningen, Groningen, The Netherlands
| | - Nils Bomer
- Department of Cardiology, University Medical Centre Groningen, University of Groningen, Groningen, The Netherlands
| | - Niels Grote Beverborg
- Department of Cardiology, University Medical Centre Groningen, University of Groningen, Groningen, The Netherlands
| | - Peter van der Meer
- Department of Cardiology, University Medical Centre Groningen, University of Groningen, Groningen, The Netherlands
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15
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Madhan S, Kalaiselvan A. Omics data classification using constitutive artificial neural network optimized with single candidate optimizer. NETWORK (BRISTOL, ENGLAND) 2024:1-25. [PMID: 38736309 DOI: 10.1080/0954898x.2024.2348726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 04/23/2024] [Indexed: 05/14/2024]
Abstract
Recent technical advancements enable omics-based biological study of molecules with very high throughput and low cost, such as genomic, proteomic, and microbionics'. To overcome this drawback, Omics Data Classification using Constitutive Artificial Neural Network Optimized with Single Candidate Optimizer (ODC-ZOA-CANN-SCO) is proposed in this manuscript. The input data is pre-processing by using Adaptive variational Bayesian filtering (AVBF) to replace missing values. The pre-processing data is fed to Zebra Optimization Algorithm (ZOA) for dimensionality reduction. Then, the Constitutive Artificial Neural Network (CANN) is employed to classify omics data. The weight parameter is optimized by Single Candidate Optimizer (SCO). The proposed ODC-ZOA-CANN-SCO method attains 25.36%, 21.04%, 22.18%, 26.90%, and 28.12% higher accuracy when analysed to the existing methods like multi-omics data integration utilizing adaptive graph learning and attention mode for patient categorization with biomarker identification (MOD-AGL-AM-PABI), deep learning method depending upon multi-omics data integration to create risk stratification prediction mode for skin cutaneous melanoma (DL-MODI-RSP-SCM), Deep belief network-base model for identifying Alzheimer's disease utilizing multi-omics data (DDN-DAD-MOD), hybrid cancer prediction depending upon multi-omics data and reinforcement learning state action reward state action (HCP-MOD-RL-SARSA), machine learning basis method under omics data including biological knowledge database for cancer clinical endpoint prediction (ML-ODBKD-CCEP) methods, respectively.
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Affiliation(s)
- Subramaniam Madhan
- Department of Computer Science and Engineering, University College of Engineering, Thirukkuvalai (A Constituent College of Anna University Chennai), Nagapattinam, Tamilnadu, India
| | - Anbarasan Kalaiselvan
- Department of Science and Humanities, University College of Engineering, Thirukkuvalai (A Constituent College of Anna University Chennai), Nagapattinam, Tamilnadu, India
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16
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Valous NA, Popp F, Zörnig I, Jäger D, Charoentong P. Graph machine learning for integrated multi-omics analysis. Br J Cancer 2024:10.1038/s41416-024-02706-7. [PMID: 38729996 DOI: 10.1038/s41416-024-02706-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 04/25/2024] [Accepted: 04/26/2024] [Indexed: 05/12/2024] Open
Abstract
Multi-omics experiments at bulk or single-cell resolution facilitate the discovery of hypothesis-generating biomarkers for predicting response to therapy, as well as aid in uncovering mechanistic insights into cellular and microenvironmental processes. Many methods for data integration have been developed for the identification of key elements that explain or predict disease risk or other biological outcomes. The heterogeneous graph representation of multi-omics data provides an advantage for discerning patterns suitable for predictive/exploratory analysis, thus permitting the modeling of complex relationships. Graph-based approaches-including graph neural networks-potentially offer a reliable methodological toolset that can provide a tangible alternative to scientists and clinicians that seek ideas and implementation strategies in the integrated analysis of their omics sets for biomedical research. Graph-based workflows continue to push the limits of the technological envelope, and this perspective provides a focused literature review of research articles in which graph machine learning is utilized for integrated multi-omics data analyses, with several examples that demonstrate the effectiveness of graph-based approaches.
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Affiliation(s)
- Nektarios A Valous
- Applied Tumor Immunity Clinical Cooperation Unit, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 460, 69120, Heidelberg, Germany.
- Center for Quantitative Analysis of Molecular and Cellular Biosystems (Bioquant), Heidelberg University, Im Neuenheimer Feld 267, 69120, Heidelberg, Germany.
| | - Ferdinand Popp
- Applied Tumor Immunity Clinical Cooperation Unit, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 460, 69120, Heidelberg, Germany
- Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
| | - Inka Zörnig
- Center for Quantitative Analysis of Molecular and Cellular Biosystems (Bioquant), Heidelberg University, Im Neuenheimer Feld 267, 69120, Heidelberg, Germany
- Department of Medical Oncology, National Center for Tumor Diseases (NCT), Heidelberg University Hospital (UKHD), Im Neuenheimer Feld 460, 69120, Heidelberg, Germany
| | - Dirk Jäger
- Applied Tumor Immunity Clinical Cooperation Unit, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 460, 69120, Heidelberg, Germany
- Center for Quantitative Analysis of Molecular and Cellular Biosystems (Bioquant), Heidelberg University, Im Neuenheimer Feld 267, 69120, Heidelberg, Germany
- Department of Medical Oncology, National Center for Tumor Diseases (NCT), Heidelberg University Hospital (UKHD), Im Neuenheimer Feld 460, 69120, Heidelberg, Germany
| | - Pornpimol Charoentong
- Center for Quantitative Analysis of Molecular and Cellular Biosystems (Bioquant), Heidelberg University, Im Neuenheimer Feld 267, 69120, Heidelberg, Germany
- Department of Medical Oncology, National Center for Tumor Diseases (NCT), Heidelberg University Hospital (UKHD), Im Neuenheimer Feld 460, 69120, Heidelberg, Germany
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17
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Drouard G, Mykkänen J, Heiskanen J, Pohjonen J, Ruohonen S, Pahkala K, Lehtimäki T, Wang X, Ollikainen M, Ripatti S, Pirinen M, Raitakari O, Kaprio J. Exploring machine learning strategies for predicting cardiovascular disease risk factors from multi-omic data. BMC Med Inform Decis Mak 2024; 24:116. [PMID: 38698395 PMCID: PMC11064347 DOI: 10.1186/s12911-024-02521-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 04/29/2024] [Indexed: 05/05/2024] Open
Abstract
BACKGROUND Machine learning (ML) classifiers are increasingly used for predicting cardiovascular disease (CVD) and related risk factors using omics data, although these outcomes often exhibit categorical nature and class imbalances. However, little is known about which ML classifier, omics data, or upstream dimension reduction strategy has the strongest influence on prediction quality in such settings. Our study aimed to illustrate and compare different machine learning strategies to predict CVD risk factors under different scenarios. METHODS We compared the use of six ML classifiers in predicting CVD risk factors using blood-derived metabolomics, epigenetics and transcriptomics data. Upstream omic dimension reduction was performed using either unsupervised or semi-supervised autoencoders, whose downstream ML classifier performance we compared. CVD risk factors included systolic and diastolic blood pressure measurements and ultrasound-based biomarkers of left ventricular diastolic dysfunction (LVDD; E/e' ratio, E/A ratio, LAVI) collected from 1,249 Finnish participants, of which 80% were used for model fitting. We predicted individuals with low, high or average levels of CVD risk factors, the latter class being the most common. We constructed multi-omic predictions using a meta-learner that weighted single-omic predictions. Model performance comparisons were based on the F1 score. Finally, we investigated whether learned omic representations from pre-trained semi-supervised autoencoders could improve outcome prediction in an external cohort using transfer learning. RESULTS Depending on the ML classifier or omic used, the quality of single-omic predictions varied. Multi-omics predictions outperformed single-omics predictions in most cases, particularly in the prediction of individuals with high or low CVD risk factor levels. Semi-supervised autoencoders improved downstream predictions compared to the use of unsupervised autoencoders. In addition, median gains in Area Under the Curve by transfer learning compared to modelling from scratch ranged from 0.09 to 0.14 and 0.07 to 0.11 units for transcriptomic and metabolomic data, respectively. CONCLUSIONS By illustrating the use of different machine learning strategies in different scenarios, our study provides a platform for researchers to evaluate how the choice of omics, ML classifiers, and dimension reduction can influence the quality of CVD risk factor predictions.
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Affiliation(s)
- Gabin Drouard
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
| | - Juha Mykkänen
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
| | - Jarkko Heiskanen
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
| | - Joona Pohjonen
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
| | - Saku Ruohonen
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
| | - Katja Pahkala
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
- Paavo Nurmi Centre & Unit for Health and Physical Activity, University of Turku, Turku, Finland
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, 33520, Tampere, Finland
| | - Xiaoling Wang
- Georgia Prevention Institute, Medical College of Georgia, Augusta University, Augusta, GA, USA
| | - Miina Ollikainen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Minerva Foundation Institute for Medical Research, Helsinki, Finland
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Matti Pirinen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
| | - Olli Raitakari
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
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18
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Ewald JD, Zhou G, Lu Y, Kolic J, Ellis C, Johnson JD, Macdonald PE, Xia J. Web-based multi-omics integration using the Analyst software suite. Nat Protoc 2024; 19:1467-1497. [PMID: 38355833 DOI: 10.1038/s41596-023-00950-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 11/21/2023] [Indexed: 02/16/2024]
Abstract
The growing number of multi-omics studies demands clear conceptual workflows coupled with easy-to-use software tools to facilitate data analysis and interpretation. This protocol covers three key components involved in multi-omics analysis, including single-omics data analysis, knowledge-driven integration using biological networks and data-driven integration through joint dimensionality reduction. Using the dataset from a recent multi-omics study of human pancreatic islet tissue and plasma samples, the first section introduces how to perform transcriptomics/proteomics data analysis using ExpressAnalyst and lipidomics data analysis using MetaboAnalyst. On the basis of significant features detected in these workflows, the second section demonstrates how to perform knowledge-driven integration using OmicsNet. The last section illustrates how to perform data-driven integration from the normalized omics data and metadata using OmicsAnalyst. The complete protocol can be executed in ~2 h. Compared with other available options for multi-omics integration, the Analyst software suite described in this protocol enables researchers to perform a wide range of omics data analysis tasks via a user-friendly web interface.
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Affiliation(s)
- Jessica D Ewald
- Institute of Parasitology, McGill University, Montreal, Quebec, Canada
| | - Guangyan Zhou
- Institute of Parasitology, McGill University, Montreal, Quebec, Canada
| | - Yao Lu
- Department of Microbiology and Immunology, McGill University, Montreal, Quebec, Canada
| | - Jelena Kolic
- Life Sciences Institute, Department of Cellular and Physiological Sciences, University of British Columbia, Vancouver, British Columbia, Canada
| | - Cara Ellis
- Department of Pharmacology, University of Alberta, Edmonton, Alberta, Canada
| | - James D Johnson
- Life Sciences Institute, Department of Cellular and Physiological Sciences, University of British Columbia, Vancouver, British Columbia, Canada
| | - Patrick E Macdonald
- Department of Pharmacology, University of Alberta, Edmonton, Alberta, Canada
| | - Jianguo Xia
- Institute of Parasitology, McGill University, Montreal, Quebec, Canada.
- Department of Microbiology and Immunology, McGill University, Montreal, Quebec, Canada.
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Procopio N, Bonicelli A. From flesh to bones: Multi-omics approaches in forensic science. Proteomics 2024:e2200335. [PMID: 38683823 DOI: 10.1002/pmic.202200335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 03/12/2024] [Accepted: 03/26/2024] [Indexed: 05/02/2024]
Abstract
Recent advancements in omics techniques have revolutionised the study of biological systems, enabling the generation of high-throughput biomolecular data. These innovations have found diverse applications, ranging from personalised medicine to forensic sciences. While the investigation of multiple aspects of cells, tissues or entire organisms through the integration of various omics approaches (such as genomics, epigenomics, metagenomics, transcriptomics, proteomics and metabolomics) has already been established in fields like biomedicine and cancer biology, its full potential in forensic sciences remains only partially explored. In this review, we have presented a comprehensive overview of state-of-the-art analytical platforms employed in omics research, with specific emphasis on their application in the forensic field for the identification of the cadaver and the cause of death. Moreover, we have conducted a critical analysis of the computational integration of omics approaches, and highlighted the latest advancements in employing multi-omics techniques for forensic investigations.
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Affiliation(s)
- Noemi Procopio
- Research Centre for Field Archaeology and Experimental Taphonomy, School of Law and Policing, University of Central Lancashire, Preston, UK
| | - Andrea Bonicelli
- Research Centre for Field Archaeology and Experimental Taphonomy, School of Law and Policing, University of Central Lancashire, Preston, UK
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20
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Liu G, Wang S, Liu J, Zhang J, Pan X, Fan X, Shao T, Sun Y. Using machine learning methods to study the tumour microenvironment and its biomarkers in osteosarcoma metastasis. Heliyon 2024; 10:e29322. [PMID: 38623240 PMCID: PMC11016722 DOI: 10.1016/j.heliyon.2024.e29322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 04/04/2024] [Accepted: 04/04/2024] [Indexed: 04/17/2024] Open
Abstract
Background The long-term prognosis for patients with osteosarcoma (OS) metastasis remains unfavourable, highlighting the urgent need for research that explores potential biomarkers using innovative methodologies. Methods This study explored potential biomarkers for OS metastasis by analysing data from the Cancer Genome Atlas Program (TCGA) and Gene Expression Omnibus (GEO) databases. The synthetic minority oversampling technique (SMOTE) was employed to tackle class imbalances, while genes were selected using four feature selection algorithms (Monte Carlo feature selection [MCFS], Borota, minimum-redundancy maximum-relevance [mRMR], and light gradient-boosting machine [LightGBM]) based on the gene expression matrix. Four machine learning (ML) algorithms (support vector machine [SVM], extreme gradient boosting [XGBoost], random forest [RF], and k-nearest neighbours [kNN]) were utilized to determine the optimal number of genes for building the model. Interpretable machine learning (IML) was applied to construct prediction networks, revealing potential relationships among the selected genes. Additionally, enrichment analysis, survival analysis, and immune infiltration were performed on the featured genes. Results In DS1, DS2, and DS3, the IML algorithm identified 53, 45, and 46 features, respectively. Using the merged gene set, we obtained a total of 79 interpretable prediction rules for OS metastasis. We subsequently conducted an in-depth investigation on 39 crucial molecules associated with predicting OS metastasis, elucidating their roles within the tumour microenvironment. Importantly, we found that certain genes act as both predictors and differentially expressed genes. Finally, our study unveiled statistically significant differences in survival between the high and low expression groups of TRIP4, S100A9, SELL and SLC11A1, and there was a certain correlation between these genes and 22 various immune cells. Conclusions The biomarkers discovered in this study hold significant implications for personalized therapies, potentially enhancing the clinical prognosis of patients with OS.
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Affiliation(s)
- Guangyuan Liu
- The First Department of Orthopedic Surgery, Third Hospital of Shijiazhuang, Tiyu South Avenue No.15, Shijiazhuang, Hebei Province, China
| | - Shaochun Wang
- Department of Oncology, Shijiazhuang People's Hospital, No.365, Jian Hua Nan Da Jie, Shijiazhuang, Hebei Province, China
| | - Jinhui Liu
- The First Department of Orthopedic Surgery, Third Hospital of Shijiazhuang, Tiyu South Avenue No.15, Shijiazhuang, Hebei Province, China
| | - Jiangli Zhang
- The First Department of Orthopedic Surgery, Third Hospital of Shijiazhuang, Tiyu South Avenue No.15, Shijiazhuang, Hebei Province, China
| | - Xiqing Pan
- The First Department of Orthopedic Surgery, Third Hospital of Shijiazhuang, Tiyu South Avenue No.15, Shijiazhuang, Hebei Province, China
| | - Xiao Fan
- The First Department of Orthopedic Surgery, Third Hospital of Shijiazhuang, Tiyu South Avenue No.15, Shijiazhuang, Hebei Province, China
| | - Tingting Shao
- Department of Pediatrics, Peking University First Hospital, 8 Xishku Street, Xicheng District, Beijing, China
| | - Yi Sun
- Department of Surgery, Shijiazhuang People's Hospital, No.365, Jian Hua Nan Da Jie, Shijiazhuang, Hebei Province, China
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21
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Williams A. Multiomics data integration, limitations, and prospects to reveal the metabolic activity of the coral holobiont. FEMS Microbiol Ecol 2024; 100:fiae058. [PMID: 38653719 PMCID: PMC11067971 DOI: 10.1093/femsec/fiae058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 03/25/2024] [Accepted: 04/22/2024] [Indexed: 04/25/2024] Open
Abstract
Since their radiation in the Middle Triassic period ∼240 million years ago, stony corals have survived past climate fluctuations and five mass extinctions. Their long-term survival underscores the inherent resilience of corals, particularly when considering the nutrient-poor marine environments in which they have thrived. However, coral bleaching has emerged as a global threat to coral survival, requiring rapid advancements in coral research to understand holobiont stress responses and allow for interventions before extensive bleaching occurs. This review encompasses the potential, as well as the limits, of multiomics data applications when applied to the coral holobiont. Synopses for how different omics tools have been applied to date and their current restrictions are discussed, in addition to ways these restrictions may be overcome, such as recruiting new technology to studies, utilizing novel bioinformatics approaches, and generally integrating omics data. Lastly, this review presents considerations for the design of holobiont multiomics studies to support lab-to-field advancements of coral stress marker monitoring systems. Although much of the bleaching mechanism has eluded investigation to date, multiomic studies have already produced key findings regarding the holobiont's stress response, and have the potential to advance the field further.
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Affiliation(s)
- Amanda Williams
- Microbial Biology Graduate Program, Rutgers University, 76 Lipman Drive, New Brunswick, NJ 08901, United States
- Department of Biochemistry and Microbiology, Rutgers University, 76 Lipman Drive, New Brunswick, NJ 08901, United States
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22
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Li Z, Pei S, Wang Y, Zhang G, Lin H, Dong S. Advancing predictive markers in lung adenocarcinoma: A machine learning-based immunotherapy prognostic prediction signature. ENVIRONMENTAL TOXICOLOGY 2024. [PMID: 38591820 DOI: 10.1002/tox.24284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 03/19/2024] [Accepted: 03/31/2024] [Indexed: 04/10/2024]
Abstract
The prognosis of lung adenocarcinoma (LUAD) is generally poor. Immunotherapy has emerged as a promising therapeutic modality, demonstrating remarkable potential for substantially prolonging the overall survival of individuals afflicted with LUAD. However, there is currently a lack of reliable signatures for identifying patients who would benefit from immunotherapy. We conducted a comparative analysis of two immunotherapy cohorts (OAK and POPLAR) and utilized single-factor COX regression to identify genes that significantly impact the prognosis of LUAD. Based on the TCGA-LUAD dataset, we employed a combination of 101 machine learning algorithms to construct a model and selected the optimal model. The model was validated on five GEO datasets and compared with 144 previously published signatures to assess its performance. Subsequently, we explored the underlying biological mechanisms through tumor mutation burden analysis, enrichment analysis, and immune infiltration analysis. An immunotherapy prognostic prediction signature (IPPS) was constructed based on 13 genes, showing robust performance in the TCGA-LUAD dataset. IPPS exhibited consistent predictive accuracy in the validation cohorts. Compared to 144 previously published signatures, IPPS consistently ranked among the top in terms of C-index values. Further exploration revealed differences between high and low-IPPS groups in terms of tumor mutation burden, pathway enrichment, and immune infiltration. IPPS demonstrates strong predictive capabilities for the prognosis of LUAD patients, offering the potential to identify suitable candidates for immunotherapy and contribute to precision treatment strategies for LUAD.
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Affiliation(s)
- Zhongyan Li
- Department of Geriatric Medicine, The Affiliated Huai'an Hospital of Yangzhou University
| | - Shengbin Pei
- Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yanjuan Wang
- Department of Gastroenterology, The First Afliated Hospital of Nanjing Medical University, Nanjing, China
| | - Ge Zhang
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Haoran Lin
- Department of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Shiyang Dong
- Department of Thoracic Surgery, Fuyang Tumor Hospital, Fuyang, China
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23
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Zhang W, Mou M, Hu W, Lu M, Zhang H, Zhang H, Luo Y, Xu H, Tao L, Dai H, Gao J, Zhu F. MOINER: A Novel Multiomics Early Integration Framework for Biomedical Classification and Biomarker Discovery. J Chem Inf Model 2024; 64:2720-2732. [PMID: 38373720 DOI: 10.1021/acs.jcim.4c00013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Abstract
In the context of precision medicine, multiomics data integration provides a comprehensive understanding of underlying biological processes and is critical for disease diagnosis and biomarker discovery. One commonly used integration method is early integration through concatenation of multiple dimensionally reduced omics matrices due to its simplicity and ease of implementation. However, this approach is seriously limited by information loss and lack of latent feature interaction. Herein, a novel multiomics early integration framework (MOINER) based on information enhancement and image representation learning is thus presented to address the challenges. MOINER employs the self-attention mechanism to capture the intrinsic correlations of omics-features, which make it significantly outperform the existing state-of-the-art methods for multiomics data integration. Moreover, visualizing the attention embedding and identifying potential biomarkers offer interpretable insights into the prediction results. All source codes and model for MOINER are freely available https://github.com/idrblab/MOINER.
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Affiliation(s)
- Wei Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Wei Hu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Mingkun Lu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Hanyu Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Hongning Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Yongchao Luo
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Hongquan Xu
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Lin Tao
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Haibin Dai
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Jianqing Gao
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
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Mukherjee A, Abraham S, Singh A, Balaji S, Mukunthan KS. From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies. Mol Biotechnol 2024:10.1007/s12033-024-01133-6. [PMID: 38565775 DOI: 10.1007/s12033-024-01133-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 02/27/2024] [Indexed: 04/04/2024]
Abstract
In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for drug discovery, is underpinned by big data, a transformative force in the current era. Omics data, characterized by its heterogeneity and enormity, has ushered biological and biomedical research into the big data domain. Acknowledging the significance of integrating diverse omics data strata, known as multi-omics studies, researchers delve into the intricate interrelationships among various omics layers. This review navigates the expansive omics landscape, showcasing tailored assays for each molecular layer through genomes to metabolomes. The sheer volume of data generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging as robust tools. These datasets not only refine disease classification but also enhance diagnostics and foster the development of targeted therapeutic strategies. Through the integration of high-throughput data, the review focuses on targeting and modeling multiple disease-regulated networks, validating interactions with multiple targets, and enhancing therapeutic potential using network pharmacology approaches. Ultimately, this exploration aims to illuminate the transformative impact of multi-omics in the big data era, shaping the future of biological research.
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Affiliation(s)
- Arnab Mukherjee
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - Suzanna Abraham
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - Akshita Singh
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - S Balaji
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - K S Mukunthan
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India.
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25
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Lac L, Leung CK, Hu P. Computational frameworks integrating deep learning and statistical models in mining multimodal omics data. J Biomed Inform 2024; 152:104629. [PMID: 38552994 DOI: 10.1016/j.jbi.2024.104629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 02/26/2024] [Accepted: 03/25/2024] [Indexed: 04/04/2024]
Abstract
BACKGROUND In health research, multimodal omics data analysis is widely used to address important clinical and biological questions. Traditional statistical methods rely on the strong assumptions of distribution. Statistical methods such as testing and differential expression are commonly used in omics analysis. Deep learning, on the other hand, is an advanced computer science technique that is powerful in mining high-dimensional omics data for prediction tasks. Recently, integrative frameworks or methods have been developed for omics studies that combine statistical models and deep learning algorithms. METHODS AND RESULTS The aim of these integrative frameworks is to combine the strengths of both statistical methods and deep learning algorithms to improve prediction accuracy while also providing interpretability and explainability. This review report discusses the current state-of-the-art integrative frameworks, their limitations, and potential future directions in survival and time-to-event longitudinal analysis, dimension reduction and clustering, regression and classification, feature selection, and causal and transfer learning.
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Affiliation(s)
- Leann Lac
- Department of Computer Science, University of Manitoba, Winnipeg, Manitoba, Canada; Department of Statistics, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Carson K Leung
- Department of Computer Science, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Pingzhao Hu
- Department of Computer Science, University of Manitoba, Winnipeg, Manitoba, Canada; Department of Biochemistry, Western University, London, Ontario, Canada; Department of Computer Science, Western University, London, Ontario, Canada; Department of Oncology, Western University, London, Ontario, Canada; Department of Epidemiology and Biostatistics, Western University, London, Ontario, Canada; The Children's Health Research Institute, Lawson Health Research Institute, London, Ontario, Canada.
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26
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Rai MF, Collins KH, Lang A, Maerz T, Geurts J, Ruiz-Romero C, June RK, Ramos Y, Rice SJ, Ali SA, Pastrello C, Jurisica I, Thomas Appleton C, Rockel JS, Kapoor M. Three decades of advancements in osteoarthritis research: insights from transcriptomic, proteomic, and metabolomic studies. Osteoarthritis Cartilage 2024; 32:385-397. [PMID: 38049029 DOI: 10.1016/j.joca.2023.11.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 11/22/2023] [Accepted: 11/29/2023] [Indexed: 12/06/2023]
Abstract
OBJECTIVE Osteoarthritis (OA) is a complex disease involving contributions from both local joint tissues and systemic sources. Patient characteristics, encompassing sociodemographic and clinical variables, are intricately linked with OA rendering its understanding challenging. Technological advancements have allowed for a comprehensive analysis of transcripts, proteomes and metabolomes in OA tissues/fluids through omic analyses. The objective of this review is to highlight the advancements achieved by omic studies in enhancing our understanding of OA pathogenesis over the last three decades. DESIGN We conducted an extensive literature search focusing on transcriptomics, proteomics and metabolomics within the context of OA. Specifically, we explore how these technologies have identified individual transcripts, proteins, and metabolites, as well as distinctive endotype signatures from various body tissues or fluids of OA patients, including insights at the single-cell level, to advance our understanding of this highly complex disease. RESULTS Omic studies reveal the description of numerous individual molecules and molecular patterns within OA-associated tissues and fluids. This includes the identification of specific cell (sub)types and associated pathways that contribute to disease mechanisms. However, there remains a necessity to further advance these technologies to delineate the spatial organization of cellular subtypes and molecular patterns within OA-afflicted tissues. CONCLUSIONS Leveraging a multi-omics approach that integrates datasets from diverse molecular detection technologies, combined with patients' clinical and sociodemographic features, and molecular and regulatory networks, holds promise for identifying unique patient endophenotypes. This holistic approach can illuminate the heterogeneity among OA patients and, in turn, facilitate the development of tailored therapeutic interventions.
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Affiliation(s)
- Muhammad Farooq Rai
- Department of Anatomy and Cellular Biology, College of Medicine and Health Sciences, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Kelsey H Collins
- Department of Orthopaedic Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Annemarie Lang
- Departments of Orthopaedic Surgery and Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Tristan Maerz
- Department of Orthopaedic Surgery, University of Michigan, Ann Arbor, MI, USA
| | - Jeroen Geurts
- Rheumatology, Department of Musculoskeletal Medicine, Lausanne University Hospital, Lausanne, Switzerland
| | - Cristina Ruiz-Romero
- Grupo de Investigación de Reumatología (GIR), Unidad de Proteómica, INIBIC -Hospital Universitario A Coruña, SERGAS, Spain
| | - Ronald K June
- Department of Mechanical & Industrial Engineering, Montana State University, Bozeman, MT, USA
| | - Yolande Ramos
- Dept. Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Sarah J Rice
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Shabana Amanda Ali
- Henry Ford Health + Michigan State University Health Sciences, Detroit, MI, USA
| | - Chiara Pastrello
- Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, UHN, Toronto, ON, Canada
| | - Igor Jurisica
- Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, UHN, Toronto, ON, Canada; Departments of Medical Biophysics and Computer Science, University of Toronto, Toronto, ON, Canada
| | - C Thomas Appleton
- Department of Medicine, University of Western Ontario, London, ON, Canada
| | - Jason S Rockel
- Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, UHN, Toronto, ON, Canada
| | - Mohit Kapoor
- Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, UHN, Toronto, ON, Canada.
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Wang H, Lin K, Zhang Q, Shi J, Song X, Wu J, Zhao C, He K. HyperTMO: a trusted multi-omics integration framework based on hypergraph convolutional network for patient classification. BIOINFORMATICS (OXFORD, ENGLAND) 2024; 40:btae159. [PMID: 38530977 DOI: 10.1093/bioinformatics/btae159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 02/02/2024] [Accepted: 03/24/2024] [Indexed: 03/28/2024]
Abstract
MOTIVATION The rapid development of high-throughput biomedical technologies can provide researchers with detailed multi-omics data. The multi-omics integrated analysis approach based on machine learning contributes a more comprehensive perspective to human disease research. However, there are still significant challenges in representing single-omics data and integrating multi-omics information. RESULTS This article presents HyperTMO, a Trusted Multi-Omics integration framework based on Hypergraph convolutional network for patient classification. HyperTMO constructs hypergraph structures to represent the association between samples in single-omics data, then evidence extraction is performed by hypergraph convolutional network, and multi-omics information is integrated at an evidence level. Last, we experimentally demonstrate that HyperTMO outperforms other state-of-the-art methods in breast cancer subtype classification and Alzheimer's disease classification tasks using multi-omics data from TCGA (BRCA) and ROSMAP datasets. Importantly, HyperTMO is the first attempt to integrate hypergraph structure, evidence theory, and multi-omics integration for patient classification. Its accurate and robust properties bring great potential for applications in clinical diagnosis. AVAILABILITY AND IMPLEMENTATION HyperTMO and datasets are publicly available at https://github.com/ippousyuga/HyperTMO.
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Affiliation(s)
- Haohua Wang
- School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning 116024, China
| | - Kai Lin
- School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning 116024, China
| | - Qiang Zhang
- School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning 116024, China
| | - Jinlong Shi
- Research Center for Medical Big Data, Medical Innovation Research Division of Chinese PLA General Hospital, Beijing 100039, China
| | - Xinyu Song
- Research Center for Medical Big Data, Medical Innovation Research Division of Chinese PLA General Hospital, Beijing 100039, China
| | - Jue Wu
- Research Center for Medical Big Data, Medical Innovation Research Division of Chinese PLA General Hospital, Beijing 100039, China
| | - Chenghui Zhao
- Research Center for Medical Big Data, Medical Innovation Research Division of Chinese PLA General Hospital, Beijing 100039, China
| | - Kunlun He
- Research Center for Medical Big Data, Medical Innovation Research Division of Chinese PLA General Hospital, Beijing 100039, China
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28
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Xiao H, Zou Y, Wang J, Wan S. A Review for Artificial Intelligence Based Protein Subcellular Localization. Biomolecules 2024; 14:409. [PMID: 38672426 PMCID: PMC11048326 DOI: 10.3390/biom14040409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 03/21/2024] [Accepted: 03/25/2024] [Indexed: 04/28/2024] Open
Abstract
Proteins need to be located in appropriate spatiotemporal contexts to carry out their diverse biological functions. Mislocalized proteins may lead to a broad range of diseases, such as cancer and Alzheimer's disease. Knowing where a target protein resides within a cell will give insights into tailored drug design for a disease. As the gold validation standard, the conventional wet lab uses fluorescent microscopy imaging, immunoelectron microscopy, and fluorescent biomarker tags for protein subcellular location identification. However, the booming era of proteomics and high-throughput sequencing generates tons of newly discovered proteins, making protein subcellular localization by wet-lab experiments a mission impossible. To tackle this concern, in the past decades, artificial intelligence (AI) and machine learning (ML), especially deep learning methods, have made significant progress in this research area. In this article, we review the latest advances in AI-based method development in three typical types of approaches, including sequence-based, knowledge-based, and image-based methods. We also elaborately discuss existing challenges and future directions in AI-based method development in this research field.
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Affiliation(s)
- Hanyu Xiao
- Department of Genetics, Cell Biology and Anatomy, College of Medicine, University of Nebraska Medical Center, Omaha, NE 68198, USA;
| | - Yijin Zou
- College of Veterinary Medicine, China Agricultural University, Beijing 100193, China;
| | - Jieqiong Wang
- Department of Neurological Sciences, College of Medicine, University of Nebraska Medical Center, Omaha, NE 68198, USA;
| | - Shibiao Wan
- Department of Genetics, Cell Biology and Anatomy, College of Medicine, University of Nebraska Medical Center, Omaha, NE 68198, USA;
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29
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Muller E, Shiryan I, Borenstein E. Multi-omic integration of microbiome data for identifying disease-associated modules. Nat Commun 2024; 15:2621. [PMID: 38521774 PMCID: PMC10960825 DOI: 10.1038/s41467-024-46888-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 03/08/2024] [Indexed: 03/25/2024] Open
Abstract
Multi-omic studies of the human gut microbiome are crucial for understanding its role in disease across multiple functional layers. Nevertheless, integrating and analyzing such complex datasets poses significant challenges. Most notably, current analysis methods often yield extensive lists of disease-associated features (e.g., species, pathways, or metabolites), without capturing the multi-layered structure of the data. Here, we address this challenge by introducing "MintTea", an intermediate integration-based approach combining canonical correlation analysis extensions, consensus analysis, and an evaluation protocol. MintTea identifies "disease-associated multi-omic modules", comprising features from multiple omics that shift in concord and that collectively associate with the disease. Applied to diverse cohorts, MintTea captures modules with high predictive power, significant cross-omic correlations, and alignment with known microbiome-disease associations. For example, analyzing samples from a metabolic syndrome study, MintTea identifies a module with serum glutamate- and TCA cycle-related metabolites, along with bacterial species linked to insulin resistance. In another dataset, MintTea identifies a module associated with late-stage colorectal cancer, including Peptostreptococcus and Gemella species and fecal amino acids, in line with these species' metabolic activity and their coordinated gradual increase with cancer development. This work demonstrates the potential of advanced integration methods in generating systems-level, multifaceted hypotheses underlying microbiome-disease interactions.
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Affiliation(s)
- Efrat Muller
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
| | - Itamar Shiryan
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
| | - Elhanan Borenstein
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.
- Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel.
- Santa Fe Institute, Santa Fe, NM, USA.
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Sahriar S, Akther S, Mauya J, Amin R, Mia MS, Ruhi S, Reza MS. Unlocking stroke prediction: Harnessing projection-based statistical feature extraction with ML algorithms. Heliyon 2024; 10:e27411. [PMID: 38495193 PMCID: PMC10943390 DOI: 10.1016/j.heliyon.2024.e27411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 02/26/2024] [Accepted: 02/28/2024] [Indexed: 03/19/2024] Open
Abstract
Non-communicable diseases, such as cardiovascular disease, cancer, chronic respiratory diseases, and diabetes, are responsible for approximately 71% of all deaths worldwide. Stroke, a cerebrovascular disorder, is one of the leading contributors to this burden among the top three causes of death. Early recognition of symptoms can encourage a balanced lifestyle and provide essential information for stroke prediction. To identify a stroke patient and risk factors, machine learning (ML) is a key tool for physicians. Due to different data measurement scales and their probability distributional assumptions, ML-based algorithms struggle to detect risk factors. Furthermore, when dealing with risk factors with high-dimensional features, learning algorithms struggle with complexity. In this study, rigorous statistical tests are used to identify risk factors, and PCA-FA (Integration of Principal Components and Factors) and FPCA (Factor Based PCA) approaches are proposed for projecting suitable feature representations for improving learning algorithm performances. The study dataset consists of different clinical, lifestyle, and genetic attributes, allowing for a comprehensive analysis of potential risk factors associated with stroke, which contains 5110 patient records. Using significant test (P-value <0.05), chi-square and independent sample t-test identified age, heart_disease, hypertension, work_type, ever_married, bmi, and smoking_status as risk factors for stroke. To develop the predicting model with proposed feature extraction techniques, random forests approach provides the best results when utilizing the PCA-FA method. The best accuracy rate for this approach is 92.55%, while the AUC score is 98.15%. The prediction accuracy has increased from 2.19% to 19.03% compared to the existing work. Additionally, the prediction results is robustified and reproducible with a stacking ensemble-based classification algorithm. We also developed a web-based application to help doctors diagnose stroke risk based on the findings of this study, which could be used as an additional tool to help doctors diagnose.
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Affiliation(s)
- Saad Sahriar
- Deep Statistical Learning and Research Lab, Department of Statistics, Pabna University of Science & Technology, Pabna, 6600, Bangladesh
| | - Sanjida Akther
- Deep Statistical Learning and Research Lab, Department of Statistics, Pabna University of Science & Technology, Pabna, 6600, Bangladesh
| | - Jannatul Mauya
- Deep Statistical Learning and Research Lab, Department of Statistics, Pabna University of Science & Technology, Pabna, 6600, Bangladesh
| | - Ruhul Amin
- Deep Statistical Learning and Research Lab, Department of Statistics, Pabna University of Science & Technology, Pabna, 6600, Bangladesh
| | - Md Shahajada Mia
- Department of Statistics, Pabna University of Science & Technology, Pabna, 6600, Bangladesh
| | - Sabba Ruhi
- Department of Statistics, Pabna University of Science & Technology, Pabna, 6600, Bangladesh
| | - Md Shamim Reza
- Deep Statistical Learning and Research Lab, Department of Statistics, Pabna University of Science & Technology, Pabna, 6600, Bangladesh
- Department of Statistics, Pabna University of Science & Technology, Pabna, 6600, Bangladesh
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Feng S, Ding B, Dai Z, Yin H, Ding Y, Liu S, Zhang K, Lin H, Xiao Z, Shen Y. Cancer-associated fibroblast-secreted FGF7 as an ovarian cancer progression promoter. J Transl Med 2024; 22:280. [PMID: 38491511 PMCID: PMC10941588 DOI: 10.1186/s12967-024-05085-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 03/10/2024] [Indexed: 03/18/2024] Open
Abstract
BACKGROUND Ovarian cancer (OC) is distinguished by its aggressive nature and the limited efficacy of current treatment strategies. Recent studies have emphasized the significant role of cancer-associated fibroblasts (CAFs) in OC development and progression. METHODS Employing sophisticated machine learning techniques on bulk transcriptomic datasets, we identified fibroblast growth factor 7 (FGF7), derived from CAFs, as a potential oncogenic factor. We investigated the relationship between FGF7 expression and various clinical parameters. A series of in vitro experiments were undertaken to evaluate the effect of CAFs-derived FGF7 on OC cell activities, such as proliferation, migration, and invasion. Single-cell transcriptomic analysis was also conducted to elucidate the interaction between FGF7 and its receptor. Detailed mechanistic investigations sought to clarify the pathways through which FGF7 fosters OC progression. RESULTS Our findings indicate that higher FGF7 levels correlate with advanced tumor stages, increased vascular invasion, and poorer prognosis. CAFs-derived FGF7 significantly enhanced OC cell proliferation, migration, and invasion. Single-cell analysis and in vitro studies revealed that CAFs-derived FGF7 inhibits the ubiquitination and degradation of hypoxia-inducible factor 1 alpha (HIF-1α) via FGFR2 interaction. Activation of the FGF7/HIF-1α pathway resulted in the upregulation of mesenchymal markers and downregulation of epithelial markers. Importantly, in vivo treatment with neutralizing antibodies targeting CAFs-derived FGF7 substantially reduced tumor growth. CONCLUSION Neutralizing FGF7 in the medium or inhibiting HIF-1α signaling reversed the effects of FGF7-mediated EMT, emphasizing the dependence of FGF7-mediated EMT on HIF-1α activation. These findings suggest that targeting the FGF7/HIF-1α/EMT axis may offer new therapeutic opportunities to intervene in OC progression.
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Affiliation(s)
- Songwei Feng
- Department of Obstetrics and Gynaecology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Bo Ding
- Department of Obstetrics and Gynaecology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Zhu Dai
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Han Yin
- Department of Obstetrics and Gynaecology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Yue Ding
- Department of Obstetrics and Gynaecology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Sicong Liu
- Department of Obstetrics and Gynaecology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Ke Zhang
- Department of Obstetrics and Gynaecology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Hao Lin
- Department of Clinical Science and Research, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China.
| | - Zhongdang Xiao
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China.
| | - Yang Shen
- Department of Obstetrics and Gynaecology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China.
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Brlek P, Bulić L, Bračić M, Projić P, Škaro V, Shah N, Shah P, Primorac D. Implementing Whole Genome Sequencing (WGS) in Clinical Practice: Advantages, Challenges, and Future Perspectives. Cells 2024; 13:504. [PMID: 38534348 DOI: 10.3390/cells13060504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 03/04/2024] [Accepted: 03/11/2024] [Indexed: 03/28/2024] Open
Abstract
The integration of whole genome sequencing (WGS) into all aspects of modern medicine represents the next step in the evolution of healthcare. Using this technology, scientists and physicians can observe the entire human genome comprehensively, generating a plethora of new sequencing data. Modern computational analysis entails advanced algorithms for variant detection, as well as complex models for classification. Data science and machine learning play a crucial role in the processing and interpretation of results, using enormous databases and statistics to discover new and support current genotype-phenotype correlations. In clinical practice, this technology has greatly enabled the development of personalized medicine, approaching each patient individually and in accordance with their genetic and biochemical profile. The most propulsive areas include rare disease genomics, oncogenomics, pharmacogenomics, neonatal screening, and infectious disease genomics. Another crucial application of WGS lies in the field of multi-omics, working towards the complete integration of human biomolecular data. Further technological development of sequencing technologies has led to the birth of third and fourth-generation sequencing, which include long-read sequencing, single-cell genomics, and nanopore sequencing. These technologies, alongside their continued implementation into medical research and practice, show great promise for the future of the field of medicine.
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Affiliation(s)
- Petar Brlek
- St. Catherine Specialty Hospital, 10000 Zagreb, Croatia
- International Center for Applied Biological Research, 10000 Zagreb, Croatia
- School of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
| | - Luka Bulić
- St. Catherine Specialty Hospital, 10000 Zagreb, Croatia
| | - Matea Bračić
- St. Catherine Specialty Hospital, 10000 Zagreb, Croatia
| | - Petar Projić
- International Center for Applied Biological Research, 10000 Zagreb, Croatia
| | | | - Nidhi Shah
- Dartmouth Hitchcock Medical Center, Lebannon, NH 03766, USA
| | - Parth Shah
- Dartmouth Hitchcock Medical Center, Lebannon, NH 03766, USA
| | - Dragan Primorac
- St. Catherine Specialty Hospital, 10000 Zagreb, Croatia
- International Center for Applied Biological Research, 10000 Zagreb, Croatia
- School of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
- Medical School, University of Split, 21000 Split, Croatia
- Eberly College of Science, The Pennsylvania State University, State College, PA 16802, USA
- The Henry C. Lee College of Criminal Justice and Forensic Sciences, University of New Haven, West Haven, CT 06516, USA
- REGIOMED Kliniken, 96450 Coburg, Germany
- Medical School, University of Rijeka, 51000 Rijeka, Croatia
- Faculty of Dental Medicine and Health, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
- Medical School, University of Mostar, 88000 Mostar, Bosnia and Herzegovina
- National Forensic Sciences University, Gujarat 382007, India
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Munk K, Ilina D, Ziemba L, Brader G, Molin EM. Holomics - a user-friendly R shiny application for multi-omics data integration and analysis. BMC Bioinformatics 2024; 25:93. [PMID: 38438871 PMCID: PMC10913680 DOI: 10.1186/s12859-024-05719-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 02/26/2024] [Indexed: 03/06/2024] Open
Abstract
An organism's observable traits, or phenotype, result from intricate interactions among genes, proteins, metabolites and the environment. External factors, such as associated microorganisms, along with biotic and abiotic stressors, can significantly impact this complex biological system, influencing processes like growth, development and productivity. A comprehensive analysis of the entire biological system and its interactions is thus crucial to identify key components that support adaptation to stressors and to discover biomarkers applicable in breeding programs or disease diagnostics. Since the genomics era, several other 'omics' disciplines have emerged, and recent advances in high-throughput technologies have facilitated the generation of additional omics datasets. While traditionally analyzed individually, the last decade has seen an increase in multi-omics data integration and analysis strategies aimed at achieving a holistic understanding of interactions across different biological layers. Despite these advances, the analysis of multi-omics data is still challenging due to their scale, complexity, high dimensionality and multimodality. To address these challenges, a number of analytical tools and strategies have been developed, including clustering and differential equations, which require advanced knowledge in bioinformatics and statistics. Therefore, this study recognizes the need for user-friendly tools by introducing Holomics, an accessible and easy-to-use R shiny application with multi-omics functions tailored for scientists with limited bioinformatics knowledge. Holomics provides a well-defined workflow, starting with the upload and pre-filtering of single-omics data, which are then further refined by single-omics analysis focusing on key features. Subsequently, these reduced datasets are subjected to multi-omics analyses to unveil correlations between 2-n datasets. This paper concludes with a real-world case study where microbiomics, transcriptomics and metabolomics data from previous studies that elucidate factors associated with improved sugar beet storability are integrated using Holomics. The results are discussed in the context of the biological background, underscoring the importance of multi-omics insights. This example not only highlights the versatility of Holomics in handling different types of omics data, but also validates its consistency by reproducing findings from preceding single-omics studies.
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Affiliation(s)
- Katharina Munk
- Center for Health & Bioresources, AIT Austrian Institute of Technology, Konrad-Lorenz-Straße 24, 3430, Tulln, Austria
| | - Daria Ilina
- Center for Health & Bioresources, AIT Austrian Institute of Technology, Konrad-Lorenz-Straße 24, 3430, Tulln, Austria
| | - Lisa Ziemba
- Center for Health & Bioresources, AIT Austrian Institute of Technology, Konrad-Lorenz-Straße 24, 3430, Tulln, Austria
| | - Günter Brader
- Center for Health & Bioresources, AIT Austrian Institute of Technology, Konrad-Lorenz-Straße 24, 3430, Tulln, Austria
| | - Eva M Molin
- Center for Health & Bioresources, AIT Austrian Institute of Technology, Konrad-Lorenz-Straße 24, 3430, Tulln, Austria.
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Maramraju S, Kowalczewski A, Kaza A, Liu X, Singaraju JP, Albert MV, Ma Z, Yang H. AI-organoid integrated systems for biomedical studies and applications. Bioeng Transl Med 2024; 9:e10641. [PMID: 38435826 PMCID: PMC10905559 DOI: 10.1002/btm2.10641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 12/07/2023] [Accepted: 12/13/2023] [Indexed: 03/05/2024] Open
Abstract
In this review, we explore the growing role of artificial intelligence (AI) in advancing the biomedical applications of human pluripotent stem cell (hPSC)-derived organoids. Stem cell-derived organoids, these miniature organ replicas, have become essential tools for disease modeling, drug discovery, and regenerative medicine. However, analyzing the vast and intricate datasets generated from these organoids can be inefficient and error-prone. AI techniques offer a promising solution to efficiently extract insights and make predictions from diverse data types generated from microscopy images, transcriptomics, metabolomics, and proteomics. This review offers a brief overview of organoid characterization and fundamental concepts in AI while focusing on a comprehensive exploration of AI applications in organoid-based disease modeling and drug evaluation. It provides insights into the future possibilities of AI in enhancing the quality control of organoid fabrication, label-free organoid recognition, and three-dimensional image reconstruction of complex organoid structures. This review presents the challenges and potential solutions in AI-organoid integration, focusing on the establishment of reliable AI model decision-making processes and the standardization of organoid research.
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Affiliation(s)
- Sudhiksha Maramraju
- Department of Biomedical EngineeringUniversity of North TexasDentonTexasUSA
- Texas Academy of Mathematics and ScienceUniversity of North TexasDentonTexasUSA
| | - Andrew Kowalczewski
- Department of Biomedical & Chemical EngineeringSyracuse UniversitySyracuseNew YorkUSA
- BioInspired Institute for Material and Living SystemsSyracuse UniversitySyracuseNew YorkUSA
| | - Anirudh Kaza
- Department of Biomedical EngineeringUniversity of North TexasDentonTexasUSA
- Texas Academy of Mathematics and ScienceUniversity of North TexasDentonTexasUSA
| | - Xiyuan Liu
- Department of Mechanical & Aerospace EngineeringSyracuse UniversitySyracuseNew YorkUSA
| | - Jathin Pranav Singaraju
- Department of Biomedical EngineeringUniversity of North TexasDentonTexasUSA
- Texas Academy of Mathematics and ScienceUniversity of North TexasDentonTexasUSA
| | - Mark V. Albert
- Department of Biomedical EngineeringUniversity of North TexasDentonTexasUSA
- Department of Computer Science and EngineeringUniversity of North TexasDentonTexasUSA
| | - Zhen Ma
- Department of Biomedical & Chemical EngineeringSyracuse UniversitySyracuseNew YorkUSA
- BioInspired Institute for Material and Living SystemsSyracuse UniversitySyracuseNew YorkUSA
| | - Huaxiao Yang
- Department of Biomedical EngineeringUniversity of North TexasDentonTexasUSA
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Llewellyn J, Hubbard SJ, Swift J. Translation is an emerging constraint on protein homeostasis in ageing. Trends Cell Biol 2024:S0962-8924(24)00024-2. [PMID: 38423854 DOI: 10.1016/j.tcb.2024.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 01/23/2024] [Accepted: 02/01/2024] [Indexed: 03/02/2024]
Abstract
Proteins are molecular machines that provide structure and perform vital transport, signalling and enzymatic roles. Proteins expressed by cells require tight regulation of their concentration, folding, localisation, and modifications; however, this state of protein homeostasis is continuously perturbed by tissue-level stresses. While cells in healthy tissues are able to buffer against these perturbations, for example, by expression of chaperone proteins, protein homeostasis is lost in ageing, and can lead to protein aggregation characteristic of protein folding diseases. Here, we review reports of a progressive disconnect between transcriptomic and proteomic regulation during cellular ageing. We discuss how age-associated changes to cellular responses to specific stressors in the tissue microenvironment are exacerbated by loss of ribosomal proteins, ribosomal pausing, and mistranslation.
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Affiliation(s)
- Jack Llewellyn
- Wellcome Centre for Cell-Matrix Research, Oxford Road, Manchester, M13 9PT, UK; Division of Cell Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, M13 9PT, UK
| | - Simon J Hubbard
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, M13 9PT, UK.
| | - Joe Swift
- Wellcome Centre for Cell-Matrix Research, Oxford Road, Manchester, M13 9PT, UK; Division of Cell Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, M13 9PT, UK.
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36
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Costes V, Sellem E, Marthey S, Hoze C, Bonnet A, Schibler L, Kiefer H, Jaffrezic F. Multi-omics data integration for the identification of biomarkers for bull fertility. PLoS One 2024; 19:e0298623. [PMID: 38394258 PMCID: PMC10890740 DOI: 10.1371/journal.pone.0298623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 01/26/2024] [Indexed: 02/25/2024] Open
Abstract
Bull fertility is an important economic trait, and the use of subfertile semen for artificial insemination decreases the global efficiency of the breeding sector. Although the analysis of semen functional parameters can help to identify infertile bulls, no tools are currently available to enable precise predictions and prevent the commercialization of subfertile semen. Because male fertility is a multifactorial phenotype that is dependent on genetic, epigenetic, physiological and environmental factors, we hypothesized that an integrative analysis might help to refine our knowledge and understanding of bull fertility. We combined -omics data (genotypes, sperm DNA methylation at CpGs and sperm small non-coding RNAs) and semen parameters measured on a large cohort of 98 Montbéliarde bulls with contrasting fertility levels. Multiple Factor Analysis was conducted to study the links between the datasets and fertility. Four methodologies were then considered to identify the features linked to bull fertility variation: Logistic Lasso, Random Forest, Gradient Boosting and Neural Networks. Finally, the features selected by these methods were annotated in terms of genes, to conduct functional enrichment analyses. The less relevant features in -omics data were filtered out, and MFA was run on the remaining 12,006 features, including the 11 semen parameters and a balanced proportion of each type of-omics data. The results showed that unlike the semen parameters studied the-omics datasets were related to fertility. Biomarkers related to bull fertility were selected using the four methodologies mentioned above. The most contributory CpGs, SNPs and miRNAs targeted genes were all found to be involved in development. Interestingly, fragments derived from ribosomal RNAs were overrepresented among the selected features, suggesting roles in male fertility. These markers could be used in the future to identify subfertile bulls in order to increase the global efficiency of the breeding sector.
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Affiliation(s)
- Valentin Costes
- Université Paris-Saclay, UVSQ, INRAE, BREED, Jouy-en-Josas, France
- Ecole Nationale Vétérinaire d’Alfort, BREED, Maisons-Alfort, France
- R&D Department, ELIANCE, 149 rue de Bercy, Paris, France
- Université Paris-Saclay, AgroParisTech, INRAE, GABI, Jouy-en-Josas, France
| | - Eli Sellem
- Université Paris-Saclay, UVSQ, INRAE, BREED, Jouy-en-Josas, France
- Ecole Nationale Vétérinaire d’Alfort, BREED, Maisons-Alfort, France
- R&D Department, ELIANCE, 149 rue de Bercy, Paris, France
| | - Sylvain Marthey
- Université Paris-Saclay, AgroParisTech, INRAE, GABI, Jouy-en-Josas, France
- INRAE, MaIAGE, Université Paris-Saclay, Jouy-en-Josas, France
| | - Chris Hoze
- R&D Department, ELIANCE, 149 rue de Bercy, Paris, France
- Université Paris-Saclay, AgroParisTech, INRAE, GABI, Jouy-en-Josas, France
| | - Aurélie Bonnet
- Université Paris-Saclay, UVSQ, INRAE, BREED, Jouy-en-Josas, France
- Ecole Nationale Vétérinaire d’Alfort, BREED, Maisons-Alfort, France
- R&D Department, ELIANCE, 149 rue de Bercy, Paris, France
| | | | - Hélène Kiefer
- Université Paris-Saclay, UVSQ, INRAE, BREED, Jouy-en-Josas, France
- Ecole Nationale Vétérinaire d’Alfort, BREED, Maisons-Alfort, France
| | - Florence Jaffrezic
- Université Paris-Saclay, AgroParisTech, INRAE, GABI, Jouy-en-Josas, France
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Abbasi EY, Deng Z, Ali Q, Khan A, Shaikh A, Reshan MSA, Sulaiman A, Alshahrani H. A machine learning and deep learning-based integrated multi-omics technique for leukemia prediction. Heliyon 2024; 10:e25369. [PMID: 38352790 PMCID: PMC10862685 DOI: 10.1016/j.heliyon.2024.e25369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 12/13/2023] [Accepted: 01/25/2024] [Indexed: 02/16/2024] Open
Abstract
In recent years, scientific data on cancer has expanded, providing potential for a better understanding of malignancies and improved tailored care. Advances in Artificial Intelligence (AI) processing power and algorithmic development position Machine Learning (ML) and Deep Learning (DL) as crucial players in predicting Leukemia, a blood cancer, using integrated multi-omics technology. However, realizing these goals demands novel approaches to harness this data deluge. This study introduces a novel Leukemia diagnosis approach, analyzing multi-omics data for accuracy using ML and DL algorithms. ML techniques, including Random Forest (RF), Naive Bayes (NB), Decision Tree (DT), Logistic Regression (LR), Gradient Boosting (GB), and DL methods such as Recurrent Neural Networks (RNN) and Feedforward Neural Networks (FNN) are compared. GB achieved 97 % accuracy in ML, while RNN outperformed by achieving 98 % accuracy in DL. This approach filters unclassified data effectively, demonstrating the significance of DL for leukemia prediction. The testing validation was based on 17 different features such as patient age, sex, mutation type, treatment methods, chromosomes, and others. Our study compares ML and DL techniques and chooses the best technique that gives optimum results. The study emphasizes the implications of high-throughput technology in healthcare, offering improved patient care.
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Affiliation(s)
- Erum Yousef Abbasi
- State Key Laboratory of Wireless Network Positioning and Communication Engineering Integration Research, School of Electronics Engineering, Beijing University of Posts and Telecommunications, Beijing, China
| | - Zhongliang Deng
- State Key Laboratory of Wireless Network Positioning and Communication Engineering Integration Research, School of Electronics Engineering, Beijing University of Posts and Telecommunications, Beijing, China
| | - Qasim Ali
- Department of Software Engineering, Mehran University of Engineering and Technology, Jamshoro, Pakistan
| | - Adil Khan
- State Key Laboratory of Wireless Network Positioning and Communication Engineering Integration Research, School of Electronics Engineering, Beijing University of Posts and Telecommunications, Beijing, China
| | - Asadullah Shaikh
- Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia
| | - Mana Saleh Al Reshan
- Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia
- Scientific and Engineering Research Centre, Najran University, Najran, 61441, Saudi Arabia
| | - Adel Sulaiman
- Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia
| | - Hani Alshahrani
- Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia
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Farzan R. Artificial intelligence in Immuno-genetics. Bioinformation 2024; 20:29-35. [PMID: 38352901 PMCID: PMC10859949 DOI: 10.6026/973206300200029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Revised: 01/31/2024] [Accepted: 01/31/2024] [Indexed: 02/16/2024] Open
Abstract
Rapid advancements in the field of artificial intelligence (AI) have opened up unprecedented opportunities to revolutionize various scientific domains, including immunology and genetics. Therefore, it is of interest to explore the emerging applications of AI in immunology and genetics, with the objective of enhancing our understanding of the dynamic intricacies of the immune system, disease etiology, and genetic variations. Hence, the use of AI methodologies in immunological and genetic datasets, thereby facilitating the development of innovative approaches in the realms of diagnosis, treatment, and personalized medicine is reviewed.
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Affiliation(s)
- Raed Farzan
- Department of Clinical Laboratory Sciences, College of Applied Medical Scienecs, King Saud University, Riyadh - 11433, Saudi Arabia
- Center of Excellence in Biotechnology Research, King Saud University, Riyadh - 11433, Saudi Arabia
- Medical and Molecular Genetics Research, King Saud University, Riyadh-11433, Saudi Arabia
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Luo H, Liang H, Liu H, Fan Z, Wei Y, Yao X, Cong S. TEMINET: A Co-Informative and Trustworthy Multi-Omics Integration Network for Diagnostic Prediction. Int J Mol Sci 2024; 25:1655. [PMID: 38338932 PMCID: PMC10855161 DOI: 10.3390/ijms25031655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 01/20/2024] [Accepted: 01/26/2024] [Indexed: 02/12/2024] Open
Abstract
Advancing the domain of biomedical investigation, integrated multi-omics data have shown exceptional performance in elucidating complex human diseases. However, as the variety of omics information expands, precisely perceiving the informativeness of intra- and inter-omics becomes challenging due to the intricate interrelations, thus presenting significant challenges in the integration of multi-omics data. To address this, we introduce a novel multi-omics integration approach, referred to as TEMINET. This approach enhances diagnostic prediction by leveraging an intra-omics co-informative representation module and a trustworthy learning strategy used to address inter-omics fusion. Considering the multifactorial nature of complex diseases, TEMINET utilizes intra-omics features to construct disease-specific networks; then, it applies graph attention networks and a multi-level framework to capture more collective informativeness than pairwise relations. To perceive the contribution of co-informative representations within intra-omics, we designed a trustworthy learning strategy to identify the reliability of each omics in integration. To integrate inter-omics information, a combined-beliefs fusion approach is deployed to harmonize the trustworthy representations of different omics types effectively. Our experiments across four different diseases using mRNA, methylation, and miRNA data demonstrate that TEMINET achieves advanced performance and robustness in classification tasks.
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Affiliation(s)
- Haoran Luo
- Qingdao Innovation and Development Center, Harbin Engineering University, Qingdao 266000, China; (H.L.); (Z.F.)
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China; (H.L.); (H.L.); (Y.W.)
| | - Hong Liang
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China; (H.L.); (H.L.); (Y.W.)
| | - Hongwei Liu
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China; (H.L.); (H.L.); (Y.W.)
| | - Zhoujie Fan
- Qingdao Innovation and Development Center, Harbin Engineering University, Qingdao 266000, China; (H.L.); (Z.F.)
| | - Yanhui Wei
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China; (H.L.); (H.L.); (Y.W.)
| | - Xiaohui Yao
- Qingdao Innovation and Development Center, Harbin Engineering University, Qingdao 266000, China; (H.L.); (Z.F.)
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China; (H.L.); (H.L.); (Y.W.)
| | - Shan Cong
- Qingdao Innovation and Development Center, Harbin Engineering University, Qingdao 266000, China; (H.L.); (Z.F.)
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China; (H.L.); (H.L.); (Y.W.)
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40
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Liu W, Pratte KA, Castaldi PJ, Hersh C, Bowler RP, Banaei-Kashani F, Kechris KJ. A Generalized Higher-order Correlation Analysis Framework for Multi-Omics Network Inference. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.22.576667. [PMID: 38328226 PMCID: PMC10849540 DOI: 10.1101/2024.01.22.576667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Multiple -omics (genomics, proteomics, etc.) profiles are commonly generated to gain insight into a disease or physiological system. Constructing multi-omics networks with respect to the trait(s) of interest provides an opportunity to understand relationships between molecular features but integration is challenging due to multiple data sets with high dimensionality. One approach is to use canonical correlation to integrate one or two omics types and a single trait of interest. However, these types of methods may be limited due to (1) not accounting for higher-order correlations existing among features, (2) computational inefficiency when extending to more than two omics data when using a penalty term-based sparsity method, and (3) lack of flexibility for focusing on specific correlations (e.g., omics-to-phenotype correlation versus omics-to-omics correlations). In this work, we have developed a novel multi-omics network analysis pipeline called Sparse Generalized Tensor Canonical Correlation Analysis Network Inference (SGTCCA-Net) that can effectively overcome these limitations. We also introduce an implementation to improve the summarization of networks for downstream analyses. Simulation and real-data experiments demonstrate the effectiveness of our novel method for inferring omics networks and features of interest.
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Affiliation(s)
- Weixuan Liu
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | | | - Peter J. Castaldi
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, United States
| | - Craig Hersh
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, United States
| | - Russell P. Bowler
- Division of Pulmonary Medicine, Department of Medicine, National Jewish Health, Denver, CO, USA
| | - Farnoush Banaei-Kashani
- Department of Computer Science and Engineering, College of Engineering, Design and Computing, University of Colorado Denver, Denver, CO, USA
| | - Katerina J. Kechris
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
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Tremmel R, Hofmann U, Haag M, Schaeffeler E, Schwab M. Circulating Biomarkers Instead of Genotyping to Establish Metabolizer Phenotypes. Annu Rev Pharmacol Toxicol 2024; 64:65-87. [PMID: 37585662 DOI: 10.1146/annurev-pharmtox-032023-121106] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/18/2023]
Abstract
Pharmacogenomics (PGx) enables personalized treatment for the prediction of drug response and to avoid adverse drug reactions. Currently, PGx mainly relies on the genetic information of absorption, distribution, metabolism, and excretion (ADME) targets such as drug-metabolizing enzymes or transporters to predict differences in the patient's phenotype. However, there is evidence that the phenotype-genotype concordance is limited. Thus, we discuss different phenotyping strategies using exogenous xenobiotics (e.g., drug cocktails) or endogenous compounds for phenotype prediction. In particular, minimally invasive approaches focusing on liquid biopsies offer great potential to preemptively determine metabolic and transport capacities. Early studies indicate that ADME phenotyping using exosomes released from the liver is reliable. In addition, pharmacometric modeling and artificial intelligence improve phenotype prediction. However, further prospective studies are needed to demonstrate the clinical utility of individualized treatment based on phenotyping strategies, not only relying on genetics. The present review summarizes current knowledge and limitations.
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Affiliation(s)
- Roman Tremmel
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany;
- University of Tuebingen, Tuebingen, Germany
| | - Ute Hofmann
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany;
- University of Tuebingen, Tuebingen, Germany
| | - Mathias Haag
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany;
- University of Tuebingen, Tuebingen, Germany
| | - Elke Schaeffeler
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany;
- University of Tuebingen, Tuebingen, Germany
- Cluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies," University of Tuebingen, Tuebingen, Germany
| | - Matthias Schwab
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany;
- University of Tuebingen, Tuebingen, Germany
- Cluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies," University of Tuebingen, Tuebingen, Germany
- Departments of Clinical Pharmacology, and Pharmacy and Biochemistry, University of Tuebingen, Tuebingen, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center Heidelberg (DKFZ), Partner Site, Tübingen, Germany
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Mardoc E, Sow MD, Déjean S, Salse J. Genomic data integration tutorial, a plant case study. BMC Genomics 2024; 25:66. [PMID: 38233804 PMCID: PMC10792847 DOI: 10.1186/s12864-023-09833-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 11/22/2023] [Indexed: 01/19/2024] Open
Abstract
BACKGROUND The ongoing evolution of the Next Generation Sequencing (NGS) technologies has led to the production of genomic data on a massive scale. While tools for genomic data integration and analysis are becoming increasingly available, the conceptual and analytical complexities still represent a great challenge in many biological contexts. RESULTS To address this issue, we describe a six-steps tutorial for the best practices in genomic data integration, consisting of (1) designing a data matrix; (2) formulating a specific biological question toward data description, selection and prediction; (3) selecting a tool adapted to the targeted questions; (4) preprocessing of the data; (5) conducting preliminary analysis, and finally (6) executing genomic data integration. CONCLUSION The tutorial has been tested and demonstrated on publicly available genomic data generated from poplar (Populus L.), a woody plant model. We also developed a new graphical output for the unsupervised multi-block analysis, cimDiablo_v2, available at https://forgemia.inra.fr/umr-gdec/omics-integration-on-poplar , and allowing the selection of master drivers in genomic data variation and interplay.
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Affiliation(s)
- Emile Mardoc
- UCA-INRAE UMR 1095 Genetics, Diversity and Ecophysiology of Cereals (GDEC), 5 Chemin de Beaulieu, 63000, Clermont-Ferrand, France
| | - Mamadou Dia Sow
- UCA-INRAE UMR 1095 Genetics, Diversity and Ecophysiology of Cereals (GDEC), 5 Chemin de Beaulieu, 63000, Clermont-Ferrand, France
| | - Sébastien Déjean
- Institut de Mathématiques de Toulouse, UMR 5219, Université de Toulouse, CNRS, Université Paul Sabatier, Toulouse, France
| | - Jérôme Salse
- UCA-INRAE UMR 1095 Genetics, Diversity and Ecophysiology of Cereals (GDEC), 5 Chemin de Beaulieu, 63000, Clermont-Ferrand, France.
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Palzer EF, Safo SE. mvlearnR and Shiny App for multiview learning. BIOINFORMATICS ADVANCES 2024; 4:vbae005. [PMID: 38304121 PMCID: PMC10833139 DOI: 10.1093/bioadv/vbae005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 11/20/2023] [Accepted: 01/14/2024] [Indexed: 02/03/2024]
Abstract
Summary The package mvlearnR and accompanying Shiny App is intended for integrating data from multiple sources or views or modalities (e.g. genomics, proteomics, clinical, and demographic data). Most existing software packages for multiview learning are decentralized and offer limited capabilities, making it difficult for users to perform comprehensive integrative analysis. The new package wraps statistical and machine learning methods and graphical tools, providing a convenient and easy data integration workflow. For users with limited programming language, we provide a Shiny Application to facilitate data integration anywhere and on any device. The methods have potential to offer deeper insights into complex disease mechanisms. Availability and implementation mvlearnR is available from the following GitHub repository: https://github.com/lasandrall/mvlearnR. The web application is hosted on shinyapps.io and available at: https://multi-viewlearn.shinyapps.io/MultiView_Modeling/.
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Affiliation(s)
- Elise F Palzer
- Division of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, Minnesota 55414, United States
| | - Sandra E Safo
- Division of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, Minnesota 55414, United States
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Tong L, Shi W, Isgut M, Zhong Y, Lais P, Gloster L, Sun J, Swain A, Giuste F, Wang MD. Integrating Multi-Omics Data With EHR for Precision Medicine Using Advanced Artificial Intelligence. IEEE Rev Biomed Eng 2024; 17:80-97. [PMID: 37824325 DOI: 10.1109/rbme.2023.3324264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2023]
Abstract
With the recent advancement of novel biomedical technologies such as high-throughput sequencing and wearable devices, multi-modal biomedical data ranging from multi-omics molecular data to real-time continuous bio-signals are generated at an unprecedented speed and scale every day. For the first time, these multi-modal biomedical data are able to make precision medicine close to a reality. However, due to data volume and the complexity, making good use of these multi-modal biomedical data requires major effort. Researchers and clinicians are actively developing artificial intelligence (AI) approaches for data-driven knowledge discovery and causal inference using a variety of biomedical data modalities. These AI-based approaches have demonstrated promising results in various biomedical and healthcare applications. In this review paper, we summarize the state-of-the-art AI models for integrating multi-omics data and electronic health records (EHRs) for precision medicine. We discuss the challenges and opportunities in integrating multi-omics data with EHRs and future directions. We hope this review can inspire future research and developing in integrating multi-omics data with EHRs for precision medicine.
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Chetty A, Blekhman R. Multi-omic approaches for host-microbiome data integration. Gut Microbes 2024; 16:2297860. [PMID: 38166610 PMCID: PMC10766395 DOI: 10.1080/19490976.2023.2297860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 12/18/2023] [Indexed: 01/05/2024] Open
Abstract
The gut microbiome interacts with the host through complex networks that affect physiology and health outcomes. It is becoming clear that these interactions can be measured across many different omics layers, including the genome, transcriptome, epigenome, metabolome, and proteome, among others. Multi-omic studies of the microbiome can provide insight into the mechanisms underlying host-microbe interactions. As more omics layers are considered, increasingly sophisticated statistical methods are required to integrate them. In this review, we provide an overview of approaches currently used to characterize multi-omic interactions between host and microbiome data. While a large number of studies have generated a deeper understanding of host-microbiome interactions, there is still a need for standardization across approaches. Furthermore, microbiome studies would also benefit from the collection and curation of large, publicly available multi-omics datasets.
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Affiliation(s)
- Ashwin Chetty
- Committee on Genetics, Genomics and Systems Biology, The University of Chicago, Chicago, IL, USA
| | - Ran Blekhman
- Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA
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Zeng Z, Quan C, Zhou S, Gong S, Iqbal M, Kulyar MFEA, Nawaz S, Li K, Li J. Gut microbiota and metabolic modulation by supplementation of polysaccharide-producing Bacillus licheniformis from Tibetan Yaks: A comprehensive multi-omics analysis. Int J Biol Macromol 2024; 254:127808. [PMID: 37926310 DOI: 10.1016/j.ijbiomac.2023.127808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 10/23/2023] [Accepted: 10/30/2023] [Indexed: 11/07/2023]
Abstract
Gut microbiota and their metabolic processes depend on the intricate interplay of gut microbiota and their metabolic processes. Bacillus licheniformis, a beneficial food supplement, has shown promising effects on stabilizing gut microbiota and metabolites. However, the precise mechanisms underlying these effects remain elusive. In this study, we investigated the impact of polysaccharide-producing B. licheniformis as a dietary supplement on the gut microbiome and metabolites through a combination of scanning electron microscopy (SEM), histological analysis, high-throughput sequencing (HTS), and metabolomics. Our findings revealed that the B. licheniformis-treated group exhibited significantly increased jejunal goblet cells. Moreover, gut microbial diversity was lower in the treatment group as compared to the control, accompanied by noteworthy shifts in the abundance of specific bacterial taxa. Enrichment of Firmicutes, Lachnospiraceae, and Clostridiales_bacterium contrasted with reduced levels of Campylobacterota, Proteobacteria, Parasutterella, and Helicobacter. Notably, the treatment group showed significant weight gain after 33 days, emphasizing the polysaccharide's impact on host metabolism. Delving into gut metabolomics, we discovered significant alterations in metabolites. Nine metabolites, including olprinone, pyruvic acid, and 2-methyl-3-oxopropanoate, were upregulated, while eleven, including defoslimod and voclosporin were down-regulated, shedding light on phenylpropanoid biosynthesis, tricarboxylic acid cycle (TCA cycle), and the glucagon signaling pathway. This comprehensive multi-omics analysis offers compelling insights into the potential of B. licheniformis as a dietary polysaccharide supplement for gut health and host metabolism, promising significant implications for gut-related issues.
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Affiliation(s)
- Zhibo Zeng
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan 430070, PR China; Institute of Agricultural Sciences, ETH Zurich, Universitaetstrasse 2, 8092 Zurich, Switzerland
| | - Chuxian Quan
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan 430070, PR China
| | - Shimeng Zhou
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan 430070, PR China
| | - Saisai Gong
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan 430070, PR China
| | - Mudassar Iqbal
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan 430070, PR China; Faculty of Veterinary and Animal Sciences, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan
| | | | - Shah Nawaz
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan 430070, PR China
| | - Kewei Li
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan 430070, PR China
| | - Jiakui Li
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan 430070, PR China; College of Animals Husbandry and Veterinary Medicine, Tibet Agricultural and Animal Husbandry University, Linzhi, Tibet 860000, PR China.
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Jurenaite N, León-Periñán D, Donath V, Torge S, Jäkel R. SetQuence & SetOmic: Deep set transformers for whole genome and exome tumour analysis. Biosystems 2024; 235:105095. [PMID: 38065399 DOI: 10.1016/j.biosystems.2023.105095] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 10/17/2023] [Accepted: 11/28/2023] [Indexed: 12/21/2023]
Abstract
In oncology, Deep Learning has shown great potential to personalise tasks such as tumour type classification, based on per-patient omics data-sets. Being high dimensional, incorporation of such data in one model is a challenge, often leading to one-dimensional studies and, therefore, information loss. Instead, we first propose relying on non-fixed sets of whole genome or whole exome variant-associated sequences, which can be used for supervised learning of oncology-relevant tasks by our Set Transformer based Deep Neural Network, SetQuence. We optimise this architecture to improve its efficiency. This allows for exploration of not just coding but also non-coding variants, from large datasets. Second, we extend the model to incorporate these representations together with multiple other sources of omics data in a flexible way with SetOmic. Evaluation, using these representations, shows improved robustness and reduced information loss compared to previous approaches, while still being computationally tractable. By means of Explainable Artificial Intelligence methods, our models are able to recapitulate the biological contribution of highly attributed features in the tumours studied. This validation opens the door to novel directions in multi-faceted genome and exome wide biomarker discovery and personalised treatment among other presently clinically relevant tasks.
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Affiliation(s)
- Neringa Jurenaite
- Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), TU Dresden, Chemnitzer Str 46b, Dresden, 01187, Saxony, Germany.
| | - Daniel León-Periñán
- Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), TU Dresden, Chemnitzer Str 46b, Dresden, 01187, Saxony, Germany; Max-Delbrück-Centrum für Molekulare Medizin, Hannoversche Str. 28, Berlin, 10115, Germany.
| | - Veronika Donath
- Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), TU Dresden, Chemnitzer Str 46b, Dresden, 01187, Saxony, Germany.
| | - Sunna Torge
- Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), TU Dresden, Chemnitzer Str 46b, Dresden, 01187, Saxony, Germany.
| | - René Jäkel
- Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), TU Dresden, Chemnitzer Str 46b, Dresden, 01187, Saxony, Germany.
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Wang H, Han X, Ren J, Cheng H, Li H, Li Y, Li X. A prognostic prediction model for ovarian cancer using a cross-modal view correlation discovery network. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:736-764. [PMID: 38303441 DOI: 10.3934/mbe.2024031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
Ovarian cancer is a tumor with different clinicopathological and molecular features, and the vast majority of patients have local or extensive spread at the time of diagnosis. Early diagnosis and prognostic prediction of patients can contribute to the understanding of the underlying pathogenesis of ovarian cancer and the improvement of therapeutic outcomes. The occurrence of ovarian cancer is influenced by multiple complex mechanisms, including the genome, transcriptome and proteome. Different types of omics analysis help predict the survival rate of ovarian cancer patients. Multi-omics data of ovarian cancer exhibit high-dimensional heterogeneity, and existing methods for integrating multi-omics data have not taken into account the variability and inter-correlation between different omics data. In this paper, we propose a deep learning model, MDCADON, which utilizes multi-omics data and cross-modal view correlation discovery network. We introduce random forest into LASSO regression for feature selection on mRNA expression, DNA methylation, miRNA expression and copy number variation (CNV), aiming to select important features highly correlated with ovarian cancer prognosis. A multi-modal deep neural network is used to comprehensively learn feature representations of each omics data and clinical data, and cross-modal view correlation discovery network is employed to construct the multi-omics discovery tensor, exploring the inter-relationships between different omics data. The experimental results demonstrate that MDCADON is superior to the existing methods in predicting ovarian cancer prognosis, which enables survival analysis for patients and facilitates the determination of follow-up treatment plans. Finally, we perform Gene Ontology (GO) term analysis and biological pathway analysis on the genes identified by MDCADON, revealing the underlying mechanisms of ovarian cancer and providing certain support for guiding ovarian cancer treatments.
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Affiliation(s)
- Huiqing Wang
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China
| | - Xiao Han
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China
| | - Jianxue Ren
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China
| | - Hao Cheng
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China
| | - Haolin Li
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China
| | - Ying Li
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China
| | - Xue Li
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China
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Heylen D, Peeters J, Aerts J, Ertaylan G, Hooyberghs J. BioMOBS: A multi-omics visual analytics workflow for biomolecular insight generation. PLoS One 2023; 18:e0295361. [PMID: 38096184 PMCID: PMC10721075 DOI: 10.1371/journal.pone.0295361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 11/19/2023] [Indexed: 12/17/2023] Open
Abstract
One of the challenges in multi-omics data analysis for precision medicine is the efficient exploration of undiscovered molecular interactions in disease processes. We present BioMOBS, a workflow consisting of two data visualization tools integrated with an open-source molecular information database to perform clinically relevant analyses (https://github.com/driesheylen123/BioMOBS). We performed exploratory pathway analysis with BioMOBS and demonstrate its ability to generate relevant molecular hypotheses, by reproducing recent findings in type 2 diabetes UK biobank data. The central visualisation tool, where data-driven and literature-based findings can be integrated, is available within the github link as well. BioMOBS is a workflow that leverages information from multiple data-driven interactive analyses and visually integrates it with established pathway knowledge. The demonstrated use cases place trust in the usage of BioMOBS as a procedure to offer clinically relevant insights in disease pathway analyses on various types of omics data.
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Affiliation(s)
- Dries Heylen
- Theory Lab, Data Science Institute (DSI), Hasselt University, Diepenbeek, Belgium
- Flemish Institute for Technological Research (VITO), Mol, Belgium
| | - Jannes Peeters
- Data Science Institute (DSI), Hasselt University, Diepenbeek, Belgium
| | - Jan Aerts
- Visual Data Analysis Lab, Department of Biostystems KU Leuven, Leuven, Belgium
| | - Gökhan Ertaylan
- Flemish Institute for Technological Research (VITO), Mol, Belgium
| | - Jef Hooyberghs
- Theory Lab, Data Science Institute (DSI), Hasselt University, Diepenbeek, Belgium
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Chao H, Zhang S, Hu Y, Ni Q, Xin S, Zhao L, Ivanisenko VA, Orlov YL, Chen M. Integrating omics databases for enhanced crop breeding. J Integr Bioinform 2023; 20:jib-2023-0012. [PMID: 37486120 PMCID: PMC10777369 DOI: 10.1515/jib-2023-0012] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 06/12/2023] [Indexed: 07/25/2023] Open
Abstract
Crop plant breeding involves selecting and developing new plant varieties with desirable traits such as increased yield, improved disease resistance, and enhanced nutritional value. With the development of high-throughput technologies, such as genomics, transcriptomics, and metabolomics, crop breeding has entered a new era. However, to effectively use these technologies, integration of multi-omics data from different databases is required. Integration of omics data provides a comprehensive understanding of the biological processes underlying plant traits and their interactions. This review highlights the importance of integrating omics databases in crop plant breeding, discusses available omics data and databases, describes integration challenges, and highlights recent developments and potential benefits. Taken together, the integration of omics databases is a critical step towards enhancing crop plant breeding and improving global food security.
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Affiliation(s)
- Haoyu Chao
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou310058, China
| | - Shilong Zhang
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou310058, China
| | - Yueming Hu
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou310058, China
| | - Qingyang Ni
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou310058, China
| | - Saige Xin
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou310058, China
| | - Liang Zhao
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou310058, China
| | - Vladimir A. Ivanisenko
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk630090, Russia
| | - Yuriy L. Orlov
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk630090, Russia
- Agrarian and Technological Institute, Peoples’ Friendship University of Russia, Moscow117198, Russia
- The Digital Health Institute, I.M. Sechenov First Moscow State Medical University of the Russian Ministry of Health (Sechenov University), Moscow119991, Russia
| | - Ming Chen
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou310058, China
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