1
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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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Zemelka-Wiacek M, Agache I, Akdis CA, Akdis M, Casale TB, Dramburg S, Jahnz-Różyk K, Kosowska A, Matricardi PM, Pfaar O, Shamji MH, Jutel M. Hot topics in allergen immunotherapy, 2023: Current status and future perspective. Allergy 2024; 79:823-842. [PMID: 37984449 DOI: 10.1111/all.15945] [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: 08/29/2023] [Revised: 10/10/2023] [Accepted: 11/04/2023] [Indexed: 11/22/2023]
Abstract
The importance of allergen immunotherapy (AIT) is multifaceted, encompassing both clinical and quality-of-life improvements and cost-effectiveness in the long term. Key mechanisms of allergen tolerance induced by AIT include changes in memory type allergen-specific T- and B-cell responses towards a regulatory phenotype with decreased Type 2 responses, suppression of allergen-specific IgE and increased IgG1 and IgG4, decreased mast cell and eosinophil numbers in allergic tissues and increased activation thresholds. The potential of novel patient enrolment strategies for AIT is taking into account recent advances in biomarkers discoveries, molecular allergy diagnostics and mobile health applications contributing to a personalized approach enhancement that can increase AIT efficacy and compliance. Artificial intelligence can help manage and interpret complex and heterogeneous data, including big data from omics and non-omics research, potentially predict disease subtypes, identify biomarkers and monitor patient responses to AIT. Novel AIT preparations, such as synthetic compounds, innovative carrier systems and adjuvants, are also of great promise. Advances in clinical trial models, including adaptive, complex and hybrid designs as well as real-world evidence, allow more flexibility and cost reduction. The analyses of AIT cost-effectiveness show a clear long-term advantage compared to pharmacotherapy. Important research questions, such as defining clinical endpoints, biomarkers of patient selection and efficacy, mechanisms and the modulation of the placebo effect and alternatives to conventional field trials, including allergen exposure chamber studies are still to be elucidated. This review demonstrates that AIT is still in its growth phase and shows immense development prospects.
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Affiliation(s)
| | - Ioana Agache
- Faculty of Medicine, Transylvania University, Brasov, Romania
| | - Cezmi A Akdis
- Swiss Institute of Allergy and Asthma Research (SIAF), University Zurich, Davos, Switzerland
| | - Mübeccel Akdis
- Swiss Institute of Allergy and Asthma Research (SIAF), University Zurich, Davos, Switzerland
| | - Thomas B Casale
- Departments of Medicine and Pediatrics and Division of Allergy and Immunology, Joy McCann Culverhouse Clinical Research Center, University of South Florida, Tampa, Florida, USA
| | - Stephanie Dramburg
- Department of Pediatric Respiratory Care, Immunology and Critical Care Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Karina Jahnz-Różyk
- Department of Internal Diseases, Pneumonology, Allergology and Clinical Immunology, Military Institute of Medicine-National Research Institute, Warsaw, Poland
| | - Anna Kosowska
- Department of Clinical Immunology, Wroclaw Medical University, Wroclaw, Poland
- ALL-MED Medical Research Institute, Wroclaw, Poland
| | - Paolo M Matricardi
- Department of Pediatric Respiratory Care, Immunology and Critical Care Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Oliver Pfaar
- Section of Rhinology and Allergy, Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Marburg, Philipps-Universität Marburg, Marburg, Germany
| | - Mohamed H Shamji
- Allergy and Clinical Immunology, National Heart and Lung Institute, Imperial College London, London, UK
| | - Marek Jutel
- Department of Clinical Immunology, Wroclaw Medical University, Wroclaw, Poland
- ALL-MED Medical Research Institute, Wroclaw, Poland
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Na AY, Lee H, Min EK, Paudel S, Choi SY, Sim H, Liu KH, Kim KT, Bae JS, Lee S. Novel Time-dependent Multi-omics Integration in Sepsis-associated Liver Dysfunction. GENOMICS, PROTEOMICS & BIOINFORMATICS 2023; 21:1101-1116. [PMID: 37084954 PMCID: PMC11082264 DOI: 10.1016/j.gpb.2023.04.002] [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: 01/14/2022] [Revised: 02/03/2023] [Accepted: 04/11/2023] [Indexed: 04/23/2023]
Abstract
The recently developed technologies that allow the analysis of each single omics have provided an unbiased insight into ongoing disease processes. However, it remains challenging to specify the study design for the subsequent integration strategies that can associate sepsis pathophysiology and clinical outcomes. Here, we conducted a time-dependent multi-omics integration (TDMI) in a sepsis-associated liver dysfunction (SALD) model. We successfully deduced the relation of the Toll-like receptor 4 (TLR4) pathway with SALD. Although TLR4 is a critical factor in sepsis progression, it is not specified in single-omics analyses but only in the TDMI analysis. This finding indicates that the TDMI-based approach is more advantageous than single-omics analyses in terms of exploring the underlying pathophysiological mechanism of SALD. Furthermore, TDMI-based approach can be an ideal paradigm for insightful biological interpretations of multi-omics datasets that will potentially reveal novel insights into basic biology, health, and diseases, thus allowing the identification of promising candidates for therapeutic strategies.
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Affiliation(s)
- Ann-Yae Na
- Research Institute of Pharmaceutical Sciences, Kyungpook National University, Daegu 41566, Republic of Korea
| | - Hyojin Lee
- Department of Environmental Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea
| | - Eun Ki Min
- Department of Environmental Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea
| | - Sanjita Paudel
- Research Institute of Pharmaceutical Sciences, Kyungpook National University, Daegu 41566, Republic of Korea; BK21 FOUR Community-Based Intelligent Novel Drug Discovery Education Unit, College of Pharmacy, Kyungpook National University, Daegu 41566, Republic of Korea
| | - So Young Choi
- Research Institute of Pharmaceutical Sciences, Kyungpook National University, Daegu 41566, Republic of Korea; BK21 FOUR Community-Based Intelligent Novel Drug Discovery Education Unit, College of Pharmacy, Kyungpook National University, Daegu 41566, Republic of Korea
| | - HyunChae Sim
- Research Institute of Pharmaceutical Sciences, Kyungpook National University, Daegu 41566, Republic of Korea; BK21 FOUR Community-Based Intelligent Novel Drug Discovery Education Unit, College of Pharmacy, Kyungpook National University, Daegu 41566, Republic of Korea
| | - Kwang-Hyeon Liu
- Research Institute of Pharmaceutical Sciences, Kyungpook National University, Daegu 41566, Republic of Korea; BK21 FOUR Community-Based Intelligent Novel Drug Discovery Education Unit, College of Pharmacy, Kyungpook National University, Daegu 41566, Republic of Korea
| | - Ki-Tae Kim
- Department of Environmental Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea
| | - Jong-Sup Bae
- Research Institute of Pharmaceutical Sciences, Kyungpook National University, Daegu 41566, Republic of Korea; BK21 FOUR Community-Based Intelligent Novel Drug Discovery Education Unit, College of Pharmacy, Kyungpook National University, Daegu 41566, Republic of Korea
| | - Sangkyu Lee
- Research Institute of Pharmaceutical Sciences, Kyungpook National University, Daegu 41566, Republic of Korea; BK21 FOUR Community-Based Intelligent Novel Drug Discovery Education Unit, College of Pharmacy, Kyungpook National University, Daegu 41566, Republic of Korea; School of Pharmacy, Sungkyunkwan University, Suwon 16419, Republic of Korea.
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Shields PG. Role of untargeted omics biomarkers of exposure and effect for tobacco research. ADDICTION NEUROSCIENCE 2023; 7:100098. [PMID: 37396411 PMCID: PMC10310069 DOI: 10.1016/j.addicn.2023.100098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Tobacco research remains a clear priority to improve individual and population health, and has recently become more complex with emerging combustible and noncombustible tobacco products. The use of omics methods in prevention and cessation studies are intended to identify new biomarkers for risk, compared risks related to other products and never use, and compliance for cessation and reinitation. to assess the relative effects of tobacco products to each other. They are important for the prediction of reinitiation of tobacco use and relapse prevention. In the research setting, both technical and clinical validation is required, which presents a number of complexities in the omics methodologies from biospecimen collection and sample preparation to data collection and analysis. When the results identify differences in omics features, networks or pathways, it is unclear if the results are toxic effects, a healthy response to a toxic exposure or neither. The use of surrogate biospecimens (e.g., urine, blood, sputum or nasal) may or may not reflect target organs such as the lung or bladder. This review describes the approaches for the use of omics in tobacco research and provides examples of prior studies, along with the strengths and limitations of the various methods. To date, there is little consistency in results, likely due to small number of studies, limitations in study size, the variability in the analytic platforms and bioinformatic pipelines, differences in biospecimen collection and/or human subject study design. Given the demonstrated value for the use of omics in clinical medicine, it is anticipated that the use in tobacco research will be similarly productive.
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Affiliation(s)
- Peter G. Shields
- Comprehensive Cancer Center, The Ohio State University and James Cancer Hospital, Columbus, OH
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Mu C, Zhao Q, Zhao Q, Yang L, Pang X, Liu T, Li X, Wang B, Fung SY, Cao H. Multi-omics in Crohn's disease: New insights from inside. Comput Struct Biotechnol J 2023; 21:3054-3072. [PMID: 37273853 PMCID: PMC10238466 DOI: 10.1016/j.csbj.2023.05.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 05/10/2023] [Accepted: 05/11/2023] [Indexed: 06/06/2023] Open
Abstract
Crohn's disease (CD) is an inflammatory bowel disease (IBD) with complex clinical manifestations such as chronic diarrhea, weight loss and hematochezia. Despite the increasing incidence worldwide, cure of CD remains extremely difficult. The rapid development of high-throughput sequencing technology with integrated-omics analyses in recent years has provided a new means for exploring the pathogenesis, mining the biomarkers and designing targeted personalized therapeutics of CD. Host genomics and epigenomics unveil heredity-related mechanisms of susceptible individuals, while microbiome and metabolomics map host-microbe interactions in CD patients. Proteomics shows great potential in searching for promising biomarkers. Nonetheless, single omics technology cannot holistically connect the mechanisms with heterogeneity of pathological behavior in CD. The rise of multi-omics analysis integrates genetic/epigenetic profiles with protein/microbial metabolite functionality, providing new hope for comprehensive and in-depth exploration of CD. Herein, we emphasized the different omics features and applications of CD and discussed the current research and limitations of multi-omics in CD. This review will update and deepen our understanding of CD from integration of broad omics spectra and will provide new evidence for targeted individualized therapeutics.
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Affiliation(s)
- Chenlu Mu
- Department of Gastroenterology and Hepatology, General Hospital, Tianjin Medical University, Tianjin Institute of Digestive Diseases, Tianjin Key Laboratory of Digestive Diseases, Tianjin, China
| | - Qianjing Zhao
- Department of Gastroenterology and Hepatology, General Hospital, Tianjin Medical University, Tianjin Institute of Digestive Diseases, Tianjin Key Laboratory of Digestive Diseases, Tianjin, China
| | - Qing Zhao
- Department of Gastroenterology and Hepatology, General Hospital, Tianjin Medical University, Tianjin Institute of Digestive Diseases, Tianjin Key Laboratory of Digestive Diseases, Tianjin, China
| | - Lijiao Yang
- Department of Gastroenterology and Hepatology, General Hospital, Tianjin Medical University, Tianjin Institute of Digestive Diseases, Tianjin Key Laboratory of Digestive Diseases, Tianjin, China
| | - Xiaoqi Pang
- Department of Gastroenterology and Hepatology, General Hospital, Tianjin Medical University, Tianjin Institute of Digestive Diseases, Tianjin Key Laboratory of Digestive Diseases, Tianjin, China
| | - Tianyu Liu
- Department of Gastroenterology and Hepatology, General Hospital, Tianjin Medical University, Tianjin Institute of Digestive Diseases, Tianjin Key Laboratory of Digestive Diseases, Tianjin, China
| | - Xiaomeng Li
- Department of Immunology, Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, School of Basic Medical Science, Tianjin Medical University, Tianjin, China
| | - Bangmao Wang
- Department of Gastroenterology and Hepatology, General Hospital, Tianjin Medical University, Tianjin Institute of Digestive Diseases, Tianjin Key Laboratory of Digestive Diseases, Tianjin, China
| | - Shan-Yu Fung
- Department of Immunology, Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, School of Basic Medical Science, Tianjin Medical University, Tianjin, China
| | - Hailong Cao
- Department of Gastroenterology and Hepatology, General Hospital, Tianjin Medical University, Tianjin Institute of Digestive Diseases, Tianjin Key Laboratory of Digestive Diseases, Tianjin, China
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Sopic M, Robinson EL, Emanueli C, Srivastava P, Angione C, Gaetano C, Condorelli G, Martelli F, Pedrazzini T, Devaux Y. Integration of epigenetic regulatory mechanisms in heart failure. Basic Res Cardiol 2023; 118:16. [PMID: 37140699 PMCID: PMC10158703 DOI: 10.1007/s00395-023-00986-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 03/27/2023] [Accepted: 04/10/2023] [Indexed: 05/05/2023]
Abstract
The number of "omics" approaches is continuously growing. Among others, epigenetics has appeared as an attractive area of investigation by the cardiovascular research community, notably considering its association with disease development. Complex diseases such as cardiovascular diseases have to be tackled using methods integrating different omics levels, so called "multi-omics" approaches. These approaches combine and co-analyze different levels of disease regulation. In this review, we present and discuss the role of epigenetic mechanisms in regulating gene expression and provide an integrated view of how these mechanisms are interlinked and regulate the development of cardiac disease, with a particular attention to heart failure. We focus on DNA, histone, and RNA modifications, and discuss the current methods and tools used for data integration and analysis. Enhancing the knowledge of these regulatory mechanisms may lead to novel therapeutic approaches and biomarkers for precision healthcare and improved clinical outcomes.
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Affiliation(s)
- Miron Sopic
- Department of Medical Biochemistry, Faculty of Pharmacy, University of Belgrade, Belgrade, Serbia
| | - Emma L Robinson
- Division of Cardiology, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Costanza Emanueli
- National Heart & Lung Institute, Imperial College London, London, UK
| | | | - Claudio Angione
- School of Computing, Engineering & Digital Technologies, Teesside University, Tees Valley, Middlesbrough, TS1 3BA, UK
- Centre for Digital Innovation, Teesside University, Campus Heart, Tees Valley, Middlesbrough, TS1 3BX, UK
- National Horizons Centre, Darlington, DL1 1HG, UK
| | - Carlo Gaetano
- Laboratorio di Epigenetica, Istituti Clinici Scientifici Maugeri IRCCS, Via Maugeri 10, 27100, Pavia, Italy
| | - Gianluigi Condorelli
- IRCCS-Humanitas Research Hospital, Via Manzoni 56, 20089, Rozzano, MI, Italy
- Institute of Genetic and Biomedical Research, National Research Council of Italy, Arnold-Heller-Str.3, 24105, Milan, Italy
| | - Fabio Martelli
- Molecular Cardiology Laboratory, IRCCS-Policlinico San Donato, Via Morandi 30, San Donato Milanese, 20097, Milan, Italy
| | - Thierry Pedrazzini
- Experimental Cardiology Unit, Division of Cardiology, Department of Cardiovascular Medicine, University of Lausanne Medical School, 1011, Lausanne, Switzerland
| | - Yvan Devaux
- Cardiovascular Research Unit, Department of Population Health, Luxembourg Institute of Health, L-1445, Strassen, Luxembourg.
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Al-Tashi Q, Saad MB, Muneer A, Qureshi R, Mirjalili S, Sheshadri A, Le X, Vokes NI, Zhang J, Wu J. Machine Learning Models for the Identification of Prognostic and Predictive Cancer Biomarkers: A Systematic Review. Int J Mol Sci 2023; 24:7781. [PMID: 37175487 PMCID: PMC10178491 DOI: 10.3390/ijms24097781] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/10/2023] [Accepted: 04/19/2023] [Indexed: 05/15/2023] Open
Abstract
The identification of biomarkers plays a crucial role in personalized medicine, both in the clinical and research settings. However, the contrast between predictive and prognostic biomarkers can be challenging due to the overlap between the two. A prognostic biomarker predicts the future outcome of cancer, regardless of treatment, and a predictive biomarker predicts the effectiveness of a therapeutic intervention. Misclassifying a prognostic biomarker as predictive (or vice versa) can have serious financial and personal consequences for patients. To address this issue, various statistical and machine learning approaches have been developed. The aim of this study is to present an in-depth analysis of recent advancements, trends, challenges, and future prospects in biomarker identification. A systematic search was conducted using PubMed to identify relevant studies published between 2017 and 2023. The selected studies were analyzed to better understand the concept of biomarker identification, evaluate machine learning methods, assess the level of research activity, and highlight the application of these methods in cancer research and treatment. Furthermore, existing obstacles and concerns are discussed to identify prospective research areas. We believe that this review will serve as a valuable resource for researchers, providing insights into the methods and approaches used in biomarker discovery and identifying future research opportunities.
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Affiliation(s)
- Qasem Al-Tashi
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Maliazurina B. Saad
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Amgad Muneer
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Rizwan Qureshi
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimization, Torrens University Australia, Fortitude Valley, Brisbane, QLD 4006, Australia
- Yonsei Frontier Lab, Yonsei University, Seoul 03722, Republic of Korea
- University Research and Innovation Center, Obuda University, 1034 Budapest, Hungary
| | - Ajay Sheshadri
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Xiuning Le
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Natalie I. Vokes
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jianjun Zhang
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jia Wu
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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8
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Flores JE, Claborne DM, Weller ZD, Webb-Robertson BJM, Waters KM, Bramer LM. Missing data in multi-omics integration: Recent advances through artificial intelligence. Front Artif Intell 2023; 6:1098308. [PMID: 36844425 PMCID: PMC9949722 DOI: 10.3389/frai.2023.1098308] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 01/23/2023] [Indexed: 02/11/2023] Open
Abstract
Biological systems function through complex interactions between various 'omics (biomolecules), and a more complete understanding of these systems is only possible through an integrated, multi-omic perspective. This has presented the need for the development of integration approaches that are able to capture the complex, often non-linear, interactions that define these biological systems and are adapted to the challenges of combining the heterogenous data across 'omic views. A principal challenge to multi-omic integration is missing data because all biomolecules are not measured in all samples. Due to either cost, instrument sensitivity, or other experimental factors, data for a biological sample may be missing for one or more 'omic techologies. Recent methodological developments in artificial intelligence and statistical learning have greatly facilitated the analyses of multi-omics data, however many of these techniques assume access to completely observed data. A subset of these methods incorporate mechanisms for handling partially observed samples, and these methods are the focus of this review. We describe recently developed approaches, noting their primary use cases and highlighting each method's approach to handling missing data. We additionally provide an overview of the more traditional missing data workflows and their limitations; and we discuss potential avenues for further developments as well as how the missing data issue and its current solutions may generalize beyond the multi-omics context.
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Affiliation(s)
- Javier E. Flores
- Pacific Northwest National Laboratory, Biological Sciences Division, Earth and Biological Sciences Directorate, Richland, WA, United States
| | - Daniel M. Claborne
- Pacific Northwest National Laboratory, Artificial Intelligence and Data Analytics Division, National Security Directorate, Richland, WA, United States
| | - Zachary D. Weller
- Pacific Northwest National Laboratory, Artificial Intelligence and Data Analytics Division, National Security Directorate, Richland, WA, United States
| | - Bobbie-Jo M. Webb-Robertson
- Pacific Northwest National Laboratory, Biological Sciences Division, Earth and Biological Sciences Directorate, Richland, WA, United States
| | - Katrina M. Waters
- Pacific Northwest National Laboratory, Biological Sciences Division, Earth and Biological Sciences Directorate, Richland, WA, United States
| | - Lisa M. Bramer
- Pacific Northwest National Laboratory, Biological Sciences Division, Earth and Biological Sciences Directorate, Richland, WA, United States,*Correspondence: Lisa M. Bramer ✉
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9
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Patra P, B R D, Kundu P, Das M, Ghosh A. Recent advances in machine learning applications in metabolic engineering. Biotechnol Adv 2023; 62:108069. [PMID: 36442697 DOI: 10.1016/j.biotechadv.2022.108069] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Revised: 10/18/2022] [Accepted: 11/22/2022] [Indexed: 11/27/2022]
Abstract
Metabolic engineering encompasses several widely-used strategies, which currently hold a high seat in the field of biotechnology when its potential is manifesting through a plethora of research and commercial products with a strong societal impact. The genomic revolution that occurred almost three decades ago has initiated the generation of large omics-datasets which has helped in gaining a better understanding of cellular behavior. The itinerary of metabolic engineering that has occurred based on these large datasets has allowed researchers to gain detailed insights and a reasonable understanding of the intricacies of biosystems. However, the existing trail-and-error approaches for metabolic engineering are laborious and time-intensive when it comes to the production of target compounds with high yields through genetic manipulations in host organisms. Machine learning (ML) coupled with the available metabolic engineering test instances and omics data brings a comprehensive and multidisciplinary approach that enables scientists to evaluate various parameters for effective strain design. This vast amount of biological data should be standardized through knowledge engineering to train different ML models for providing accurate predictions in gene circuits designing, modification of proteins, optimization of bioprocess parameters for scaling up, and screening of hyper-producing robust cell factories. This review briefs on the premise of ML, followed by mentioning various ML methods and algorithms alongside the numerous omics datasets available to train ML models for predicting metabolic outcomes with high-accuracy. The combinative interplay between the ML algorithms and biological datasets through knowledge engineering have guided the recent advancements in applications such as CRISPR/Cas systems, gene circuits, protein engineering, metabolic pathway reconstruction, and bioprocess engineering. Finally, this review addresses the probable challenges of applying ML in metabolic engineering which will guide the researchers toward novel techniques to overcome the limitations.
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Affiliation(s)
- Pradipta Patra
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Disha B R
- B.M.S College of Engineering, Basavanagudi, Bengaluru, Karnataka 560019, India
| | - Pritam Kundu
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Manali Das
- School of Bioscience, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Amit Ghosh
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India; P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, West Bengal 721302, India.
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10
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Mouse tissue glycome atlas 2022 highlights inter-organ variation in major N-glycan profiles. Sci Rep 2022; 12:17804. [PMID: 36280747 PMCID: PMC9592591 DOI: 10.1038/s41598-022-21758-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 09/30/2022] [Indexed: 01/19/2023] Open
Abstract
This study presents "mouse tissue glycome atlas" representing the profiles of major N-glycans of mouse glycoproteins that may define their essential functions in the surface glycocalyx of mouse organs/tissues and serum-derived extracellular vesicles (exosomes). Cell surface glycocalyx composed of a variety of N-glycans attached covalently to the membrane proteins, notably characteristic "N-glycosylation patterns" of the glycocalyx, plays a critical role for the regulation of cell differentiation, cell adhesion, homeostatic immune response, and biodistribution of secreted exosomes. Given that the integrity of cell surface glycocalyx correlates significantly with maintenance of the cellular morphology and homeostatic immune functions, dynamic alterations of N-glycosylation patterns in the normal glycocalyx caused by cellular abnormalities may serve as highly sensitive and promising biomarkers. Although it is believed that inter-organs variations in N-glycosylation patterns exist, information of the glycan diversity in mouse organs/tissues remains to be elusive. Here we communicate for the first-time N-glycosylation patterns of 16 mouse organs/tissues, serum, and serum-derived exosomes of Slc:ddY mice using an established solid-phase glycoblotting platform for the rapid, easy, and high throughput MALDI-TOFMS-based quantitative glycomics. The present results elicited occurrence of the organ/tissue-characteristic N-glycosylation patterns that can be discriminated to each other. Basic machine learning analysis using this N-glycome dataset enabled classification between 16 mouse organs/tissues with the highest F1 score (69.7-100%) when neural network algorithm was used. A preliminary examination demonstrated that machine learning analysis of mouse lung N-glycome dataset by random forest algorithm allows for the discrimination of lungs among the different mouse strains such as the outbred mouse Slc:ddY, inbred mouse DBA/2Crslc, and systemic lupus erythematosus model mouse MRL-lpr/lpr with the highest F1 score (74.5-83.8%). Our results strongly implicate importance of "human organ/tissue glycome atlas" for understanding the crucial and diversified roles of glycocalyx determined by the organ/tissue-characteristic N-glycosylation patterns and the discovery research for N-glycome-based disease-specific biomarkers and therapeutic targets.
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11
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Sidak D, Schwarzerová J, Weckwerth W, Waldherr S. Interpretable machine learning methods for predictions in systems biology from omics data. Front Mol Biosci 2022; 9:926623. [PMID: 36387282 PMCID: PMC9650551 DOI: 10.3389/fmolb.2022.926623] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 08/15/2022] [Indexed: 12/02/2022] Open
Abstract
Machine learning has become a powerful tool for systems biologists, from diagnosing cancer to optimizing kinetic models and predicting the state, growth dynamics, or type of a cell. Potential predictions from complex biological data sets obtained by “omics” experiments seem endless, but are often not the main objective of biological research. Often we want to understand the molecular mechanisms of a disease to develop new therapies, or we need to justify a crucial decision that is derived from a prediction. In order to gain such knowledge from data, machine learning models need to be extended. A recent trend to achieve this is to design “interpretable” models. However, the notions around interpretability are sometimes ambiguous, and a universal recipe for building well-interpretable models is missing. With this work, we want to familiarize systems biologists with the concept of model interpretability in machine learning. We consider data sets, data preparation, machine learning methods, and software tools relevant to omics research in systems biology. Finally, we try to answer the question: “What is interpretability?” We introduce views from the interpretable machine learning community and propose a scheme for categorizing studies on omics data. We then apply these tools to review and categorize recent studies where predictive machine learning models have been constructed from non-sequential omics data.
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Affiliation(s)
- David Sidak
- Department of Functional and Evolutionary Ecology, Faculty of Life Sciences, Molecular Systems Biology (MOSYS), University of Vienna, Vienna, Austria
| | - Jana Schwarzerová
- Department of Functional and Evolutionary Ecology, Faculty of Life Sciences, Molecular Systems Biology (MOSYS), University of Vienna, Vienna, Austria
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czech Republic
| | - Wolfram Weckwerth
- Department of Functional and Evolutionary Ecology, Faculty of Life Sciences, Molecular Systems Biology (MOSYS), University of Vienna, Vienna, Austria
- Vienna Metabolomics Center (VIME), Faculty of Life Sciences, University of Vienna, Vienna, Austria
| | - Steffen Waldherr
- Department of Functional and Evolutionary Ecology, Faculty of Life Sciences, Molecular Systems Biology (MOSYS), University of Vienna, Vienna, Austria
- *Correspondence: Steffen Waldherr,
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12
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Radzikowska U, Baerenfaller K, Cornejo‐Garcia JA, Karaaslan C, Barletta E, Sarac BE, Zhakparov D, Villaseñor A, Eguiluz‐Gracia I, Mayorga C, Sokolowska M, Barbas C, Barber D, Ollert M, Chivato T, Agache I, Escribese MM. Omics technologies in allergy and asthma research: An EAACI position paper. Allergy 2022; 77:2888-2908. [PMID: 35713644 PMCID: PMC9796060 DOI: 10.1111/all.15412] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 05/30/2022] [Accepted: 06/06/2022] [Indexed: 01/27/2023]
Abstract
Allergic diseases and asthma are heterogenous chronic inflammatory conditions with several distinct complex endotypes. Both environmental and genetic factors can influence the development and progression of allergy. Complex pathogenetic pathways observed in allergic disorders present a challenge in patient management and successful targeted treatment strategies. The increasing availability of high-throughput omics technologies, such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics allows studying biochemical systems and pathophysiological processes underlying allergic responses. Additionally, omics techniques present clinical applicability by functional identification and validation of biomarkers. Therefore, finding molecules or patterns characteristic for distinct immune-inflammatory endotypes, can subsequently influence its development, progression, and treatment. There is a great potential to further increase the effectiveness of single omics approaches by integrating them with other omics, and nonomics data. Systems biology aims to simultaneously and longitudinally understand multiple layers of a complex and multifactorial disease, such as allergy, or asthma by integrating several, separated data sets and generating a complete molecular profile of the condition. With the use of sophisticated biostatistics and machine learning techniques, these approaches provide in-depth insight into individual biological systems and will allow efficient and customized healthcare approaches, called precision medicine. In this EAACI Position Paper, the Task Force "Omics technologies in allergic research" broadly reviewed current advances and applicability of omics techniques in allergic diseases and asthma research, with a focus on methodology and data analysis, aiming to provide researchers (basic and clinical) with a desk reference in the field. The potential of omics strategies in understanding disease pathophysiology and key tools to reach unmet needs in allergy precision medicine, such as successful patients' stratification, accurate disease prognosis, and prediction of treatment efficacy and successful prevention measures are highlighted.
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Affiliation(s)
- Urszula Radzikowska
- Swiss Institute of Allergy and Asthma Research (SIAF)University of ZurichDavosSwitzerland,Christine‐Kühne Center for Allergy Research and Education (CK‐CARE)DavosSwitzerland
| | - Katja Baerenfaller
- Swiss Institute of Allergy and Asthma Research (SIAF)University of ZurichDavosSwitzerland,Swiss Institute of Bioinformatics (SIB)DavosSwitzerland
| | - José Antonio Cornejo‐Garcia
- Research LaboratoryIBIMA, ARADyAL Instituto de Salud Carlos III, Regional University Hospital of Málaga, UMAMálagaSpain
| | - Cagatay Karaaslan
- Department of Biology, Molecular Biology SectionFaculty of ScienceHacettepe UniversityAnkaraTurkey
| | - Elena Barletta
- Swiss Institute of Allergy and Asthma Research (SIAF)University of ZurichDavosSwitzerland,Swiss Institute of Bioinformatics (SIB)DavosSwitzerland
| | - Basak Ezgi Sarac
- Department of Biology, Molecular Biology SectionFaculty of ScienceHacettepe UniversityAnkaraTurkey
| | - Damir Zhakparov
- Swiss Institute of Allergy and Asthma Research (SIAF)University of ZurichDavosSwitzerland,Swiss Institute of Bioinformatics (SIB)DavosSwitzerland
| | - Alma Villaseñor
- Centre for Metabolomics and Bioanalysis (CEMBIO)Department of Chemistry and BiochemistryFacultad de FarmaciaUniversidad San Pablo‐CEU, CEU UniversitiesMadridSpain,Institute of Applied Molecular Medicine Nemesio Diaz (IMMAND)Department of Basic Medical SciencesFacultad de MedicinaUniversidad San Pablo CEU, CEU UniversitiesMadridSpain
| | - Ibon Eguiluz‐Gracia
- Allergy UnitHospital Regional Universitario de MálagaMálagaSpain,Allergy Research GroupInstituto de Investigación Biomédica de Málaga‐IBIMAMálagaSpain
| | - Cristobalina Mayorga
- Allergy UnitHospital Regional Universitario de MálagaMálagaSpain,Allergy Research GroupInstituto de Investigación Biomédica de Málaga‐IBIMAMálagaSpain,Andalusian Centre for Nanomedicine and Biotechnology – BIONANDMálagaSpain
| | - Milena Sokolowska
- Swiss Institute of Allergy and Asthma Research (SIAF)University of ZurichDavosSwitzerland,Christine‐Kühne Center for Allergy Research and Education (CK‐CARE)DavosSwitzerland
| | - Coral Barbas
- Centre for Metabolomics and Bioanalysis (CEMBIO)Department of Chemistry and BiochemistryFacultad de FarmaciaUniversidad San Pablo‐CEU, CEU UniversitiesMadridSpain
| | - Domingo Barber
- Institute of Applied Molecular Medicine Nemesio Diaz (IMMAND)Department of Basic Medical SciencesFacultad de MedicinaUniversidad San Pablo CEU, CEU UniversitiesMadridSpain
| | - Markus Ollert
- Department of Infection and ImmunityLuxembourg Institute of HealthyEsch‐sur‐AlzetteLuxembourg,Department of Dermatology and Allergy CenterOdense Research Center for AnaphylaxisOdense University Hospital, University of Southern DenmarkOdenseDenmark
| | - Tomas Chivato
- Institute of Applied Molecular Medicine Nemesio Diaz (IMMAND)Department of Basic Medical SciencesFacultad de MedicinaUniversidad San Pablo CEU, CEU UniversitiesMadridSpain,Department of Clinic Medical SciencesFacultad de MedicinaUniversidad San Pablo CEU, CEU UniversitiesMadridSpain
| | | | - Maria M. Escribese
- Institute of Applied Molecular Medicine Nemesio Diaz (IMMAND)Department of Basic Medical SciencesFacultad de MedicinaUniversidad San Pablo CEU, CEU UniversitiesMadridSpain
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13
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Molecular Markers of Kidney Transplantation Outcome: Current Omics Tools and Future Developments. Int J Mol Sci 2022; 23:ijms23116318. [PMID: 35682996 PMCID: PMC9181061 DOI: 10.3390/ijms23116318] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 05/31/2022] [Accepted: 06/01/2022] [Indexed: 02/04/2023] Open
Abstract
Purpose of review: The emerging field of molecular predictive medicine is aiming to change the traditional medical approach in renal transplantation. Many studies have explored potential biomarker molecules with predictive properties in renal transplantation, issued from omics research. Herein, we review the biomarker molecules of four technologies (i.e., Genomics, Transcriptomics, Proteomics, and Metabolomics) associated with favorable kidney transplant outcomes. Recent findings: Several panels of molecules have been associated with the outcome that the majority of markers are related to inflammation and immune response; although. other molecular ontologies are also represented, such as proteasome, growth, regeneration, and drug metabolism. Throughout this review, we highlight the lack of properly validated statistical demonstration. Indeed, the most preeminent molecular panels either remain at the limited size study stage or are not confirmed during large-scale studies. At the core of this problem, we identify the methodological shortcomings and propose a comprehensive workflow for discovery and validation of molecular biomarkers that aims to improve the relevance of these tools in the future. Summary: Overall, adopting a patient management through omics approach could bring remarkable improvement to transplantation success. An increased effort and investment between scientists, medical biologists, and clinicians seem to be the path toward a proper solution.
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14
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Onogi A, Arakawa A. An R package VIGoR for joint estimation of multiple linear learners with variational Bayesian inference. Bioinformatics 2022; 38:3306-3309. [PMID: 35575313 PMCID: PMC9191213 DOI: 10.1093/bioinformatics/btac328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 04/30/2022] [Accepted: 05/11/2022] [Indexed: 11/13/2022] Open
Abstract
SUMMARY An R package that can implement multiple linear learners, including penalized regression and regression with spike and slab priors, in a single model has been developed. Solutions are obtained with fast minorize-maximization algorithms in the framework of variational Bayesian inference. This package helps to incorporate multimodal and high-dimensional explanatory variables in a single regression model. AVAILABILITY AND IMPLEMENTATION The R package VIGoR (Variational Bayesian Inference for Genome-wide Regression) is available at the Comprehensive R Archive Network (CRAN) (https://cran.r-project.org/) and at github (https://github.com/Onogi/VIGoR). SUPPLEMENTARY INFORMATION Supplementary Materials are provided at the journal homepage. R scripts to reproduce the experiment results and pdf manual of the package are provided at https://github.com/Onogi/VIGoR.
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Affiliation(s)
- Akio Onogi
- Department of Plant Life Science, Faculty of Agriculture, Ryukoku University, 1-5, Yokotani, Seta, Oe-cho, Otsu, Shiga, 520-2194, Japan
| | - Aisaku Arakawa
- Division of Animal Breeding and Reproduction Research, Institute of Livestock and Grassland Science, National Agriculture and Food Research Organization, Tsukuba, Ibaraki, 305-0901, Japan
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15
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Youn J, Rai N, Tagkopoulos I. Knowledge integration and decision support for accelerated discovery of antibiotic resistance genes. Nat Commun 2022; 13:2360. [PMID: 35487919 PMCID: PMC9055065 DOI: 10.1038/s41467-022-29993-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 03/04/2022] [Indexed: 11/09/2022] Open
Abstract
We present a machine learning framework to automate knowledge discovery through knowledge graph construction, inconsistency resolution, and iterative link prediction. By incorporating knowledge from 10 publicly available sources, we construct an Escherichia coli antibiotic resistance knowledge graph with 651,758 triples from 23 triple types after resolving 236 sets of inconsistencies. Iteratively applying link prediction to this graph and wet-lab validation of the generated hypotheses reveal 15 antibiotic resistant E. coli genes, with 6 of them never associated with antibiotic resistance for any microbe. Iterative link prediction leads to a performance improvement and more findings. The probability of positive findings highly correlates with experimentally validated findings (R2 = 0.94). We also identify 5 homologs in Salmonella enterica that are all validated to confer resistance to antibiotics. This work demonstrates how evidence-driven decisions are a step toward automating knowledge discovery with high confidence and accelerated pace, thereby substituting traditional time-consuming and expensive methods.
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Affiliation(s)
- Jason Youn
- Department of Computer Science, University of California, Davis, CA, 95616, USA
- Genome Center, University of California, Davis, CA, 95616, USA
- USDA/NSF AI Institute for Next Generation Food Systems (AIFS), University of California, Davis, CA, 95616, USA
| | - Navneet Rai
- Department of Computer Science, University of California, Davis, CA, 95616, USA
- Genome Center, University of California, Davis, CA, 95616, USA
- USDA/NSF AI Institute for Next Generation Food Systems (AIFS), University of California, Davis, CA, 95616, USA
| | - Ilias Tagkopoulos
- Department of Computer Science, University of California, Davis, CA, 95616, USA.
- Genome Center, University of California, Davis, CA, 95616, USA.
- USDA/NSF AI Institute for Next Generation Food Systems (AIFS), University of California, Davis, CA, 95616, USA.
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16
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Sampaio M, Rocha M, Dias O. Exploring synergies between plant metabolic modelling and machine learning. Comput Struct Biotechnol J 2022; 20:1885-1900. [PMID: 35521559 PMCID: PMC9052043 DOI: 10.1016/j.csbj.2022.04.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 04/08/2022] [Accepted: 04/11/2022] [Indexed: 11/03/2022] Open
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17
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Novel data archival system for multi-omics data of human exposure to harmful substances. Mol Cell Toxicol 2022. [DOI: 10.1007/s13273-022-00226-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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18
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Chalise U, Daseke MJ, Kalusche WJ, Konfrst SR, Rodriguez-Paar JR, Flynn ER, Cook LM, Becirovic-Agic M, Lindsey ML. Macrophages secrete murinoglobulin-1 and galectin-3 to regulate neutrophil degranulation after myocardial infarction. Mol Omics 2022; 18:186-195. [PMID: 35230372 PMCID: PMC8963000 DOI: 10.1039/d1mo00519g] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 02/11/2022] [Indexed: 02/03/2023]
Abstract
Inflammation presides early after myocardial infarction (MI) as a key event in cardiac wound healing. Ischemic cardiomyocytes secrete inflammatory cues to stimulate infiltration of leukocytes, predominantly macrophages and neutrophils. Infiltrating neutrophils degranulate to release a series of proteases including matrix metalloproteinase (MMP)-9 to break down extracellular matrix and remove necrotic myocytes to create space for the infarct scar to form. While neutrophil to macrophage communication has been explored, the reverse has been understudied. We used a proteomics approach to catalogue the macrophage secretome at MI day 1. Murinoglobulin-1 (MUG1) was the highest-ranked secreted protein (4.1-fold upregulated at MI day 1 vs. day 0 pre-MI cardiac macrophages, p = 0.004). By transcriptomics evaluation, galectin-3 (Lgals3) was 2.2-fold upregulated (p = 0.008) in MI day 1 macrophages. We explored the direct roles of MUG1 and Lgals3 on neutrophil degranulation. MUG1 blunted while Lgals3 amplified neutrophil degranulation in response to phorbol 12-myristate 13-acetate or interleukin-1β, as measured by MMP-9 secretion. Lgals3 itself also stimulated MMP-9 secretion. To determine if MUG1 regulated Lgals3, we co-stimulated neutrophils with MUG1 and Lgals3. MUG1 limited degranulation stimulated by Lgals3 by 64% (p < 0.001). In vivo, MUG1 was elevated in the infarct region at MI days 1 and 3, while Lgals3 increased at MI day 7. The ratio of MUG1 to Lgals3 positively correlated with infarct wall thickness, revealing that MUG1 attenuated infarct wall thinning. In conclusion, macrophages at MI day 1 secrete MUG1 to limit and Lgals3 to accentuate neutrophil degranulation to regulate infarct wall thinning.
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Affiliation(s)
- Upendra Chalise
- Department of Cellular and Integrative Physiology, Center for Heart and Vascular Research, University of Nebraska Medical Center, Omaha, NE, 68198, USA.
- Research Service, Nebraska-Western Iowa Health Care System, Omaha, NE, 68105, USA
| | - Michael J Daseke
- Department of Cellular and Integrative Physiology, Center for Heart and Vascular Research, University of Nebraska Medical Center, Omaha, NE, 68198, USA.
- Research Service, Nebraska-Western Iowa Health Care System, Omaha, NE, 68105, USA
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, 39216, USA
| | - William J Kalusche
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, 39216, USA
| | - Shelby R Konfrst
- Department of Cellular and Integrative Physiology, Center for Heart and Vascular Research, University of Nebraska Medical Center, Omaha, NE, 68198, USA.
- Research Service, Nebraska-Western Iowa Health Care System, Omaha, NE, 68105, USA
| | - Jocelyn R Rodriguez-Paar
- Department of Cellular and Integrative Physiology, Center for Heart and Vascular Research, University of Nebraska Medical Center, Omaha, NE, 68198, USA.
- Research Service, Nebraska-Western Iowa Health Care System, Omaha, NE, 68105, USA
| | - Elizabeth R Flynn
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, 39216, USA
| | - Leah M Cook
- Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Mediha Becirovic-Agic
- Department of Cellular and Integrative Physiology, Center for Heart and Vascular Research, University of Nebraska Medical Center, Omaha, NE, 68198, USA.
- Research Service, Nebraska-Western Iowa Health Care System, Omaha, NE, 68105, USA
| | - Merry L Lindsey
- Department of Cellular and Integrative Physiology, Center for Heart and Vascular Research, University of Nebraska Medical Center, Omaha, NE, 68198, USA.
- Research Service, Nebraska-Western Iowa Health Care System, Omaha, NE, 68105, USA
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19
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Viaud G, Mayilvahanan P, Cournede PH. Representation Learning for the Clustering of Multi-Omics Data. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:135-145. [PMID: 33600320 DOI: 10.1109/tcbb.2021.3060340] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The integration of several sources of data for the identification of subtypes of diseases has gained attention over the past few years. The heterogeneity and the high dimensions of the data sets calls for an adequate representation of the data. We summarize the field of representation learning for the multi-omics clustering problem and we investigate several techniques to learn relevant combined representations, using methods from group factor analysis (PCA, MFA and extensions) and from machine learning with autoencoders. We highlight the importance of appropriately designing and training the latter, notably with a novel combination of a disjointed deep autoencoder (DDAE) architecture and a layer-wise reconstruction loss. These different representations can then be clustered to identify biologically meaningful clusters of patients. We provide a unifying framework for model comparison between statistical and deep learning approaches with the introduction of a new weighted internal clustering index that evaluates how well the clustering information is retained from each source, favoring contributions from all data sets. We apply our methodology to two case studies for which previous works of integrative clustering exist, TCGA Breast Cancer and TARGET Neuroblastoma, and show how our method can yield good and well-balanced clusters across the different data sources.
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20
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Agregán R, Echegaray N, Nawaz A, Hano C, Gohari G, Pateiro M, Lorenzo JM. Foodomic-Based Approach for the Control and Quality Improvement of Dairy Products. Metabolites 2021; 11:818. [PMID: 34940577 PMCID: PMC8709215 DOI: 10.3390/metabo11120818] [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: 10/20/2021] [Revised: 11/20/2021] [Accepted: 11/26/2021] [Indexed: 11/16/2022] Open
Abstract
The food quality assurance before selling is a needed requirement intended for protecting consumer interests. In the same way, it is also indispensable to promote continuous improvement of sensory and nutritional properties. In this regard, food research has recently contributed with studies focused on the use of 'foodomics'. This review focuses on the use of this technology, represented by transcriptomics, proteomics, and metabolomics, for the control and quality improvement of dairy products. The complex matrix of these foods requires sophisticated technology able to extract large amounts of information with which to influence their aptitude for consumption. Thus, throughout the article, different applications of the aforementioned technologies are described and discussed in essential matters related to food quality, such as the detection of fraud and/or adulterations, microbiological safety, and the assessment and improvement of transformation industrial processes (e.g., fermentation and ripening). The magnitude of the reported results may open the door to an in-depth transformation of the most conventional analytical processes, with the introduction of new techniques that allow a greater understanding of the biochemical phenomena occurred in this type of food.
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Affiliation(s)
- Rubén Agregán
- Centro Tecnológico de la Carne de Galicia, Adva. Galicia n° 4, Parque Tecnológico de Galicia, San Cibrao das Viñas, 32900 Ourense, Spain; (R.A.); (N.E.); (M.P.)
| | - Noemí Echegaray
- Centro Tecnológico de la Carne de Galicia, Adva. Galicia n° 4, Parque Tecnológico de Galicia, San Cibrao das Viñas, 32900 Ourense, Spain; (R.A.); (N.E.); (M.P.)
| | - Asad Nawaz
- Jiangsu Key Laboratory of Crop Genetics and Physiology, College of Agriculture, Yangzhou University, Yangzhou 225009, China;
- Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou 225009, China
| | - Christophe Hano
- Laboratoire de Biologie des Ligneux et des Grandes Cultures, INRA USC1328, Orleans University, CEDEX 2, 45067 Orléans, France;
| | - Gholamreza Gohari
- Department of Horticulture, Faculty of Agriculture, University of Maragheh, Maragheh 83111-55181, Iran;
| | - Mirian Pateiro
- Centro Tecnológico de la Carne de Galicia, Adva. Galicia n° 4, Parque Tecnológico de Galicia, San Cibrao das Viñas, 32900 Ourense, Spain; (R.A.); (N.E.); (M.P.)
| | - José M. Lorenzo
- Centro Tecnológico de la Carne de Galicia, Adva. Galicia n° 4, Parque Tecnológico de Galicia, San Cibrao das Viñas, 32900 Ourense, Spain; (R.A.); (N.E.); (M.P.)
- Área de Tecnología de los Alimentos, Facultad de Ciencias de Ourense, Universidad de Vigo, 32004 Ourense, Spain
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21
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Demirel HC, Arici MK, Tuncbag N. Computational approaches leveraging integrated connections of multi-omic data toward clinical applications. Mol Omics 2021; 18:7-18. [PMID: 34734935 DOI: 10.1039/d1mo00158b] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
In line with the advances in high-throughput technologies, multiple omic datasets have accumulated to study biological systems and diseases coherently. No single omics data type is capable of fully representing cellular activity. The complexity of the biological processes arises from the interactions between omic entities such as genes, proteins, and metabolites. Therefore, multi-omic data integration is crucial but challenging. The impact of the molecular alterations in multi-omic data is not local in the neighborhood of the altered gene or protein; rather, the impact diffuses in the network and changes the functionality of multiple signaling pathways and regulation of the gene expression. Additionally, multi-omic data is high-dimensional and has background noise. Several integrative approaches have been developed to accurately interpret the multi-omic datasets, including machine learning, network-based methods, and their combination. In this review, we overview the most recent integrative approaches and tools with a focus on network-based methods. We then discuss these approaches according to their specific applications, from disease-network and biomarker identification to patient stratification, drug discovery, and repurposing.
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Affiliation(s)
- Habibe Cansu Demirel
- Graduate School of Informatics, Middle East Technical University, Ankara, 06800, Turkey
| | - Muslum Kaan Arici
- Graduate School of Informatics, Middle East Technical University, Ankara, 06800, Turkey.,Foot and Mouth Diseases Institute, Ministry of Agriculture and Forestry, Ankara, 06044, Turkey
| | - Nurcan Tuncbag
- Chemical and Biological Engineering, College of Engineering, Koc University, Istanbul, 34450, Turkey.,School of Medicine, Koc University, Istanbul, 34450, Turkey.,Koc University Research Center for Translational Medicine (KUTTAM), Istanbul, Turkey.
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22
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Kwon YW, Jo HS, Bae S, Seo Y, Song P, Song M, Yoon JH. Application of Proteomics in Cancer: Recent Trends and Approaches for Biomarkers Discovery. Front Med (Lausanne) 2021; 8:747333. [PMID: 34631760 PMCID: PMC8492935 DOI: 10.3389/fmed.2021.747333] [Citation(s) in RCA: 63] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 08/26/2021] [Indexed: 12/12/2022] Open
Abstract
Proteomics has become an important field in molecular sciences, as it provides valuable information on the identity, expression levels, and modification of proteins. For example, cancer proteomics unraveled key information in mechanistic studies on tumor growth and metastasis, which has contributed to the identification of clinically applicable biomarkers as well as therapeutic targets. Several cancer proteome databases have been established and are being shared worldwide. Importantly, the integration of proteomics studies with other omics is providing extensive data related to molecular mechanisms and target modulators. These data may be analyzed and processed through bioinformatic pipelines to obtain useful information. The purpose of this review is to provide an overview of cancer proteomics and recent advances in proteomic techniques. In particular, we aim to offer insights into current proteomics studies of brain cancer, in which proteomic applications are in a relatively early stage. This review covers applications of proteomics from the discovery of biomarkers to the characterization of molecular mechanisms through advances in technology. Moreover, it addresses global trends in proteomics approaches for translational research. As a core method in translational research, the continued development of this field is expected to provide valuable information at a scale beyond that previously seen.
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Affiliation(s)
- Yang Woo Kwon
- Neurodegenerative Diseases Research Group, Korea Brain Research Institute, Daegu, South Korea
| | - Han-Seul Jo
- Neurodegenerative Diseases Research Group, Korea Brain Research Institute, Daegu, South Korea
| | - Sungwon Bae
- Neurodegenerative Diseases Research Group, Korea Brain Research Institute, Daegu, South Korea
| | - Youngsuk Seo
- Neurodegenerative Diseases Research Group, Korea Brain Research Institute, Daegu, South Korea
| | - Parkyong Song
- Department of Convergence Medicine, Pusan National University School of Medicine, Yangsan, South Korea
| | - Minseok Song
- Department of Life Sciences, Yeungnam University, Gyeongsan, South Korea
| | - Jong Hyuk Yoon
- Neurodegenerative Diseases Research Group, Korea Brain Research Institute, Daegu, South Korea
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Chung CH, Lin DW, Eames A, Chandrasekaran S. Next-Generation Genome-Scale Metabolic Modeling through Integration of Regulatory Mechanisms. Metabolites 2021; 11:606. [PMID: 34564422 PMCID: PMC8470976 DOI: 10.3390/metabo11090606] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 09/01/2021] [Accepted: 09/03/2021] [Indexed: 12/18/2022] Open
Abstract
Genome-scale metabolic models (GEMs) are powerful tools for understanding metabolism from a systems-level perspective. However, GEMs in their most basic form fail to account for cellular regulation. A diverse set of mechanisms regulate cellular metabolism, enabling organisms to respond to a wide range of conditions. This limitation of GEMs has prompted the development of new methods to integrate regulatory mechanisms, thereby enhancing the predictive capabilities and broadening the scope of GEMs. Here, we cover integrative models encompassing six types of regulatory mechanisms: transcriptional regulatory networks (TRNs), post-translational modifications (PTMs), epigenetics, protein-protein interactions and protein stability (PPIs/PS), allostery, and signaling networks. We discuss 22 integrative GEM modeling methods and how these have been used to simulate metabolic regulation during normal and pathological conditions. While these advances have been remarkable, there remains a need for comprehensive and widespread integration of regulatory constraints into GEMs. We conclude by discussing challenges in constructing GEMs with regulation and highlight areas that need to be addressed for the successful modeling of metabolic regulation. Next-generation integrative GEMs that incorporate multiple regulatory mechanisms and their crosstalk will be invaluable for discovering cell-type and disease-specific metabolic control mechanisms.
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Affiliation(s)
- Carolina H. Chung
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; (C.H.C.); (A.E.)
| | - Da-Wei Lin
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA;
| | - Alec Eames
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; (C.H.C.); (A.E.)
| | - Sriram Chandrasekaran
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; (C.H.C.); (A.E.)
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA;
- Program in Chemical Biology, University of Michigan, Ann Arbor, MI 48109, USA
- Center for Bioinformatics and Computational Medicine, University of Michigan, Ann Arbor, MI 48109, USA
- Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI 48109, USA
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24
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Gogolev YV, Ahmar S, Akpinar BA, Budak H, Kiryushkin AS, Gorshkov VY, Hensel G, Demchenko KN, Kovalchuk I, Mora-Poblete F, Muslu T, Tsers ID, Yadav NS, Korzun V. OMICs, Epigenetics, and Genome Editing Techniques for Food and Nutritional Security. PLANTS (BASEL, SWITZERLAND) 2021; 10:1423. [PMID: 34371624 PMCID: PMC8309286 DOI: 10.3390/plants10071423] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 06/30/2021] [Accepted: 07/07/2021] [Indexed: 12/22/2022]
Abstract
The incredible success of crop breeding and agricultural innovation in the last century greatly contributed to the Green Revolution, which significantly increased yields and ensures food security, despite the population explosion. However, new challenges such as rapid climate change, deteriorating soil, and the accumulation of pollutants require much faster responses and more effective solutions that cannot be achieved through traditional breeding. Further prospects for increasing the efficiency of agriculture are undoubtedly associated with the inclusion in the breeding strategy of new knowledge obtained using high-throughput technologies and new tools in the future to ensure the design of new plant genomes and predict the desired phenotype. This article provides an overview of the current state of research in these areas, as well as the study of soil and plant microbiomes, and the prospective use of their potential in a new field of microbiome engineering. In terms of genomic and phenomic predictions, we also propose an integrated approach that combines high-density genotyping and high-throughput phenotyping techniques, which can improve the prediction accuracy of quantitative traits in crop species.
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Affiliation(s)
- Yuri V. Gogolev
- Federal Research Center Kazan Scientific Center of Russian Academy of Sciences, Kazan Institute of Biochemistry and Biophysics, 420111 Kazan, Russia;
- Federal Research Center Kazan Scientific Center of Russian Academy of Sciences, Laboratory of Plant Infectious Diseases, 420111 Kazan, Russia;
| | - Sunny Ahmar
- Institute of Biological Sciences, University of Talca, 1 Poniente 1141, Talca 3460000, Chile; (S.A.); (F.M.-P.)
| | | | - Hikmet Budak
- Montana BioAg Inc., Missoula, MT 59802, USA; (B.A.A.); (H.B.)
| | - Alexey S. Kiryushkin
- Laboratory of Cellular and Molecular Mechanisms of Plant Development, Komarov Botanical Institute of the Russian Academy of Sciences, 197376 Saint Petersburg, Russia; (A.S.K.); (K.N.D.)
| | - Vladimir Y. Gorshkov
- Federal Research Center Kazan Scientific Center of Russian Academy of Sciences, Kazan Institute of Biochemistry and Biophysics, 420111 Kazan, Russia;
- Federal Research Center Kazan Scientific Center of Russian Academy of Sciences, Laboratory of Plant Infectious Diseases, 420111 Kazan, Russia;
| | - Goetz Hensel
- Centre for Plant Genome Engineering, Institute of Plant Biochemistry, Heinrich-Heine-University, 40225 Dusseldorf, Germany;
- Centre of the Region Haná for Biotechnological and Agricultural Research, Czech Advanced Technology and Research Institute, Palacký University Olomouc, 78371 Olomouc, Czech Republic
| | - Kirill N. Demchenko
- Laboratory of Cellular and Molecular Mechanisms of Plant Development, Komarov Botanical Institute of the Russian Academy of Sciences, 197376 Saint Petersburg, Russia; (A.S.K.); (K.N.D.)
| | - Igor Kovalchuk
- Department of Biological Sciences, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada; (I.K.); (N.S.Y.)
| | - Freddy Mora-Poblete
- Institute of Biological Sciences, University of Talca, 1 Poniente 1141, Talca 3460000, Chile; (S.A.); (F.M.-P.)
| | - Tugdem Muslu
- Faculty of Engineering and Natural Sciences, Sabanci University, 34956 Istanbul, Turkey;
| | - Ivan D. Tsers
- Federal Research Center Kazan Scientific Center of Russian Academy of Sciences, Laboratory of Plant Infectious Diseases, 420111 Kazan, Russia;
| | - Narendra Singh Yadav
- Department of Biological Sciences, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada; (I.K.); (N.S.Y.)
| | - Viktor Korzun
- Federal Research Center Kazan Scientific Center of Russian Academy of Sciences, Laboratory of Plant Infectious Diseases, 420111 Kazan, Russia;
- KWS SAAT SE & Co. KGaA, Grimsehlstr. 31, 37555 Einbeck, Germany
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Fiorentino G, Visintainer R, Domenici E, Lauria M, Marchetti L. MOUSSE: Multi-Omics Using Subject-Specific SignaturEs. Cancers (Basel) 2021; 13:cancers13143423. [PMID: 34298641 PMCID: PMC8304726 DOI: 10.3390/cancers13143423] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 06/29/2021] [Accepted: 06/30/2021] [Indexed: 01/06/2023] Open
Abstract
Simple Summary Modern profiling technologies have led to relevant progress toward precision medicine and disease management. A new trend in patient classification is to integrate multiple data types for the same subjects to increase the chance of identifying meaningful phenotype groups. However, these methodologies are still in their infancy, with their performance varying widely depending on the biological conditions analyzed. We developed MOUSSE, a new unsupervised and normalization-free tool for multi-omics integration able to maintain good clustering performance across a wide range of omics data. We verified its efficiency in clustering patients based on survival for ten different cancer types. The results we obtained show a higher average score in classification performance than ten other state-of-the-art algorithms. We have further validated the method by identifying a list of biological features potentially involved in patient survival, finding a high degree of concordance with the literature. Abstract High-throughput technologies make it possible to produce a large amount of data representing different biological layers, examples of which are genomics, proteomics, metabolomics and transcriptomics. Omics data have been individually investigated to understand the molecular bases of various diseases, but this may not be sufficient to fully capture the molecular mechanisms and the multilayer regulatory processes underlying complex diseases, especially cancer. To overcome this problem, several multi-omics integration methods have been introduced but a commonly agreed standard of analysis is still lacking. In this paper, we present MOUSSE, a novel normalization-free pipeline for unsupervised multi-omics integration. The main innovations are the use of rank-based subject-specific signatures and the use of such signatures to derive subject similarity networks. A separate similarity network was derived for each omics, and the resulting networks were then carefully merged in a way that considered their informative content. We applied it to analyze survival in ten different types of cancer. We produced a meaningful clusterization of the subjects and obtained a higher average classification score than ten state-of-the-art algorithms tested on the same data. As further validation, we extracted from the subject-specific signatures a list of relevant features used for the clusterization and investigated their biological role in survival. We were able to verify that, according to the literature, these features are highly involved in cancer progression and differential survival.
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Affiliation(s)
- Giuseppe Fiorentino
- Fondazione The Microsoft Research, University of Trento Centre for Computational and Systems Biology (COSBI), 38068 Rovereto, Italy; (G.F.); (R.V.); (E.D.); (M.L.)
- Department of Cellular, Computational, and Integrative Biology (CiBio), University of Trento, 38123 Povo, Italy
| | - Roberto Visintainer
- Fondazione The Microsoft Research, University of Trento Centre for Computational and Systems Biology (COSBI), 38068 Rovereto, Italy; (G.F.); (R.V.); (E.D.); (M.L.)
| | - Enrico Domenici
- Fondazione The Microsoft Research, University of Trento Centre for Computational and Systems Biology (COSBI), 38068 Rovereto, Italy; (G.F.); (R.V.); (E.D.); (M.L.)
- Department of Cellular, Computational, and Integrative Biology (CiBio), University of Trento, 38123 Povo, Italy
| | - Mario Lauria
- Fondazione The Microsoft Research, University of Trento Centre for Computational and Systems Biology (COSBI), 38068 Rovereto, Italy; (G.F.); (R.V.); (E.D.); (M.L.)
- Department of Mathematics, University of Trento, 38123 Povo, Italy
| | - Luca Marchetti
- Fondazione The Microsoft Research, University of Trento Centre for Computational and Systems Biology (COSBI), 38068 Rovereto, Italy; (G.F.); (R.V.); (E.D.); (M.L.)
- Correspondence:
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26
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Alam MA, Qiu C, Shen H, Wang YP, Deng HW. A generalized kernel machine approach to identify higher-order composite effects in multi-view datasets, with application to adolescent brain development and osteoporosis. J Biomed Inform 2021; 120:103854. [PMID: 34237438 DOI: 10.1016/j.jbi.2021.103854] [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: 09/25/2020] [Revised: 05/28/2021] [Accepted: 06/28/2021] [Indexed: 10/20/2022]
Abstract
In recent years, a comprehensive study of complex disease with multi-view datasets (e.g., multi-omics and imaging scans) has been a focus and forefront in biomedical research. State-of-the-art biomedical technologies are enabling us to collect multi-view biomedical datasets for the study of complex diseases. While all the views of data tend to explore complementary information of disease, analysis of multi-view data with complex interactions is challenging for a deeper and holistic understanding of biological systems. In this paper, we propose a novel generalized kernel machine approach to identify higher-order composite effects in multi-view biomedical datasets (GKMAHCE). This generalized semi-parametric (a mixed-effect linear model) approach includes the marginal and joint Hadamard product of features from different views of data. The proposed kernel machine approach considers multi-view data as predictor variables to allow a more thorough and comprehensive modeling of a complex trait. We applied GKMAHCE approach to both synthesized datasets and real multi-view datasets from adolescent brain development and osteoporosis study. Our experiments demonstrate that the proposed method can effectively identify higher-order composite effects and suggest that corresponding features (genes, region of interests, and chemical taxonomies) function in a concerted effort. We show that the proposed method is more generalizable than existing ones. To promote reproducible research, the source code of the proposed method is available at.
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Affiliation(s)
- Md Ashad Alam
- Tulane Center for Biomedical Informatics and Genomics, Tulane University, New Orleans, LA 70112, USA; Division of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA 70112, USA.
| | - Chuan Qiu
- Tulane Center for Biomedical Informatics and Genomics, Tulane University, New Orleans, LA 70112, USA; Division of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Hui Shen
- Tulane Center for Biomedical Informatics and Genomics, Tulane University, New Orleans, LA 70112, USA; Division of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Yu-Ping Wang
- Tulane Center for Biomedical Informatics and Genomics, Tulane University, New Orleans, LA 70112, USA; Department of Biomedical Engineering, Tulane University, New Orleans, LA 70118, USA
| | - Hong-Wen Deng
- Tulane Center for Biomedical Informatics and Genomics, Tulane University, New Orleans, LA 70112, USA; Division of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA 70112, USA
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Reel PS, Reel S, Pearson E, Trucco E, Jefferson E. Using machine learning approaches for multi-omics data analysis: A review. Biotechnol Adv 2021; 49:107739. [PMID: 33794304 DOI: 10.1016/j.biotechadv.2021.107739] [Citation(s) in RCA: 223] [Impact Index Per Article: 74.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 03/01/2021] [Accepted: 03/25/2021] [Indexed: 02/06/2023]
Abstract
With the development of modern high-throughput omic measurement platforms, it has become essential for biomedical studies to undertake an integrative (combined) approach to fully utilise these data to gain insights into biological systems. Data from various omics sources such as genetics, proteomics, and metabolomics can be integrated to unravel the intricate working of systems biology using machine learning-based predictive algorithms. Machine learning methods offer novel techniques to integrate and analyse the various omics data enabling the discovery of new biomarkers. These biomarkers have the potential to help in accurate disease prediction, patient stratification and delivery of precision medicine. This review paper explores different integrative machine learning methods which have been used to provide an in-depth understanding of biological systems during normal physiological functioning and in the presence of a disease. It provides insight and recommendations for interdisciplinary professionals who envisage employing machine learning skills in multi-omics studies.
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Affiliation(s)
- Parminder S Reel
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, United Kingdom
| | - Smarti Reel
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, United Kingdom
| | - Ewan Pearson
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, United Kingdom
| | - Emanuele Trucco
- VAMPIRE project, Computing, School of Science and Engineering, University of Dundee, Dundee, United Kingdom
| | - Emily Jefferson
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, United Kingdom.
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28
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Pazhamala LT, Kudapa H, Weckwerth W, Millar AH, Varshney RK. Systems biology for crop improvement. THE PLANT GENOME 2021; 14:e20098. [PMID: 33949787 DOI: 10.1002/tpg2.20098] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Accepted: 03/09/2021] [Indexed: 05/19/2023]
Abstract
In recent years, generation of large-scale data from genome, transcriptome, proteome, metabolome, epigenome, and others, has become routine in several plant species. Most of these datasets in different crop species, however, were studied independently and as a result, full insight could not be gained on the molecular basis of complex traits and biological networks. A systems biology approach involving integration of multiple omics data, modeling, and prediction of the cellular functions is required to understand the flow of biological information that underlies complex traits. In this context, systems biology with multiomics data integration is crucial and allows a holistic understanding of the dynamic system with the different levels of biological organization interacting with external environment for a phenotypic expression. Here, we present recent progress made in the area of various omics studies-integrative and systems biology approaches with a special focus on application to crop improvement. We have also discussed the challenges and opportunities in multiomics data integration, modeling, and understanding of the biology of complex traits underpinning yield and stress tolerance in major cereals and legumes.
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Affiliation(s)
- Lekha T Pazhamala
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Hyderabad, 502 324, India
| | - Himabindu Kudapa
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Hyderabad, 502 324, India
| | - Wolfram Weckwerth
- Department of Ecogenomics and Systems Biology, University of Vienna, Vienna, Austria
- Vienna Metabolomics Center, University of Vienna, Vienna, Austria
| | - A Harvey Millar
- ARC Centre of Excellence in Plant Energy Biology and School of Molecular Sciences, The University of Western Australia, Perth, WA, Australia
| | - Rajeev K Varshney
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Hyderabad, 502 324, India
- State Agricultural Biotechnology Centre, Crop Research Innovation Centre, Food Futures Institute, Murdoch University, Murdoch, WA, Australia
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Herrgårdh T, Madai VI, Kelleher JD, Magnusson R, Gustafsson M, Milani L, Gennemark P, Cedersund G. Hybrid modelling for stroke care: Review and suggestions of new approaches for risk assessment and simulation of scenarios. Neuroimage Clin 2021; 31:102694. [PMID: 34000646 PMCID: PMC8141769 DOI: 10.1016/j.nicl.2021.102694] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 04/27/2021] [Accepted: 05/04/2021] [Indexed: 11/28/2022]
Abstract
Stroke is an example of a complex and multi-factorial disease involving multiple organs, timescales, and disease mechanisms. To deal with this complexity, and to realize Precision Medicine of stroke, mathematical models are needed. Such approaches include: 1) machine learning, 2) bioinformatic network models, and 3) mechanistic models. Since these three approaches have complementary strengths and weaknesses, a hybrid modelling approach combining them would be the most beneficial. However, no concrete approach ready to be implemented for a specific disease has been presented to date. In this paper, we both review the strengths and weaknesses of the three approaches, and propose a roadmap for hybrid modelling in the case of stroke care. We focus on two main tasks needed for the clinical setting: a) For stroke risk calculation, we propose a new two-step approach, where non-linear mixed effects models and bioinformatic network models yield biomarkers which are used as input to a machine learning model and b) For simulation of care scenarios, we propose a new four-step approach, which revolves around iterations between simulations of the mechanistic models and imputations of non-modelled or non-measured variables. We illustrate and discuss the different approaches in the context of Precision Medicine for stroke.
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Affiliation(s)
- Tilda Herrgårdh
- Integrative Systems Biology, Department of Biomedical Engineering, Linköping University, 58185 Linköping, Sweden
| | - Vince I Madai
- Charité Lab for Artificial Intelligence in Medicine - CLAIM, Charité University Medicine Berlin, Germany; School of Computing and Digital Technology, Faculty of Computing, Engineering and the Built Environment, Birmingham City University, Birmingham, UK
| | - John D Kelleher
- ADAPT Research Centre, Technological University Dublin, Ireland
| | - Rasmus Magnusson
- Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Sweden
| | - Mika Gustafsson
- Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Sweden
| | - Lili Milani
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Peter Gennemark
- Integrative Systems Biology, Department of Biomedical Engineering, Linköping University, 58185 Linköping, Sweden; Drug Metabolism and Pharmacokinetics, Early Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Gunnar Cedersund
- Integrative Systems Biology, Department of Biomedical Engineering, Linköping University, 58185 Linköping, Sweden.
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30
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Vatansever S, Schlessinger A, Wacker D, Kaniskan HÜ, Jin J, Zhou M, Zhang B. Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions. Med Res Rev 2021; 41:1427-1473. [PMID: 33295676 PMCID: PMC8043990 DOI: 10.1002/med.21764] [Citation(s) in RCA: 83] [Impact Index Per Article: 27.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 10/30/2020] [Accepted: 11/20/2020] [Indexed: 01/11/2023]
Abstract
Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML-driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML-powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state-of-the-art of AI/ML-guided CNS drug discovery, focusing on blood-brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties.
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Affiliation(s)
- Sezen Vatansever
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Avner Schlessinger
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Daniel Wacker
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of NeuroscienceIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - H. Ümit Kaniskan
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Jian Jin
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Ming‐Ming Zhou
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Bin Zhang
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
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31
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Zhou M, Varol A, Efferth T. Multi-omics approaches to improve malaria therapy. Pharmacol Res 2021; 167:105570. [PMID: 33766628 DOI: 10.1016/j.phrs.2021.105570] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 03/02/2021] [Accepted: 03/16/2021] [Indexed: 01/07/2023]
Abstract
Malaria contributes to the most widespread infectious diseases worldwide. Even though current drugs are commercially available, the ever-increasing drug resistance problem by malaria parasites poses new challenges in malaria therapy. Hence, searching for efficient therapeutic strategies is of high priority in malaria control. In recent years, multi-omics technologies have been extensively applied to provide a more holistic view of functional principles and dynamics of biological mechanisms. We briefly review multi-omics technologies and focus on recent malaria progress conducted with the help of various omics methods. Then, we present up-to-date advances for multi-omics approaches in malaria. Next, we describe resistance phenomena to established antimalarial drugs and underlying mechanisms. Finally, we provide insight into novel multi-omics approaches, new drugs and vaccine developments and analyze current gaps in multi-omics research. Although multi-omics approaches have been successfully used in malaria studies, they are still limited. Many gaps need to be filled to bridge the gap between basic research and treatment of malaria patients. Multi-omics approaches will foster a better understanding of the molecular mechanisms of Plasmodium that are essential for the development of novel drugs and vaccines to fight this disastrous disease.
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Affiliation(s)
- Min Zhou
- Department of Pharmaceutical Biology, Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg University, Staudinger Weg 5, 55128 Mainz, Germany
| | - Ayşegül Varol
- Department of Pharmaceutical Biology, Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg University, Staudinger Weg 5, 55128 Mainz, Germany
| | - Thomas Efferth
- Department of Pharmaceutical Biology, Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg University, Staudinger Weg 5, 55128 Mainz, Germany.
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Augustijn D, de Groot HJM, Alia A. HR-MAS NMR Applications in Plant Metabolomics. Molecules 2021; 26:molecules26040931. [PMID: 33578691 PMCID: PMC7916392 DOI: 10.3390/molecules26040931] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 02/05/2021] [Accepted: 02/06/2021] [Indexed: 12/24/2022] Open
Abstract
Metabolomics is used to reduce the complexity of plants and to understand the underlying pathways of the plant phenotype. The metabolic profile of plants can be obtained by mass spectrometry or liquid-state NMR. The extraction of metabolites from the sample is necessary for both techniques to obtain the metabolic profile. This extraction step can be eliminated by making use of high-resolution magic angle spinning (HR-MAS) NMR. In this review, an HR-MAS NMR-based workflow is described in more detail, including used pulse sequences in metabolomics. The pre-processing steps of one-dimensional HR-MAS NMR spectra are presented, including spectral alignment, baseline correction, bucketing, normalisation and scaling procedures. We also highlight some of the models which can be used to perform multivariate analysis on the HR-MAS NMR spectra. Finally, applications of HR-MAS NMR in plant metabolomics are described and show that HR-MAS NMR is a powerful tool for plant metabolomics studies.
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Affiliation(s)
- Dieuwertje Augustijn
- Leiden Institute of Chemistry, Leiden University, P.O. Box 9502, 2300 RA Leiden, The Netherlands;
- Correspondence: (D.A.); (A.A.)
| | - Huub J. M. de Groot
- Leiden Institute of Chemistry, Leiden University, P.O. Box 9502, 2300 RA Leiden, The Netherlands;
| | - A. Alia
- Leiden Institute of Chemistry, Leiden University, P.O. Box 9502, 2300 RA Leiden, The Netherlands;
- Institute of Medical Physics and Biophysics, University of Leipzig, Härtelstr. 16–17, D-04107 Leipzig, Germany
- Correspondence: (D.A.); (A.A.)
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33
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Applying precision medicine to unmet clinical needs in psoriatic disease. Nat Rev Rheumatol 2020; 16:609-627. [PMID: 33024296 DOI: 10.1038/s41584-020-00507-9] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/07/2020] [Indexed: 02/08/2023]
Abstract
Psoriatic disease (PsD) is a heterogeneous condition that can affect peripheral and axial joints (arthritis), entheses, skin (psoriasis) and other structures. Over the past decade, considerable advances have been made both in our understanding of the pathogenesis of PsD and in the treatment of its diverse manifestations. However, several major areas of continued unmet need in the care of patients with PsD have been identified. One of these areas is the prediction of poor outcome, notably radiographic outcome in patients with psoriatic arthritis, so that stratified medicine approaches can be taken; another is predicting response to the numerous current and emerging therapies for PsD, so that precision medicine can be applied to rapidly improve clinical outcome and reduce the risk of toxicity. In order to address these needs, novel approaches, including imaging, tissue analysis and the application of proteogenomic technologies, are proposed as methodological solutions that will assist the dissection of the critical immune-metabolic pathways in this complex disease. Learning from advances made in other inflammatory diseases, it is time to address these unmet needs in a multi-centre partnership aimed at improving short-term and long-term outcomes for patients with PsD.
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34
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Marie B. Disentangling of the ecotoxicological signal using "omics" analyses, a lesson from the survey of the impact of cyanobacterial proliferations on fishes. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 736:139701. [PMID: 32497891 DOI: 10.1016/j.scitotenv.2020.139701] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 05/16/2020] [Accepted: 05/23/2020] [Indexed: 06/11/2023]
Abstract
Omics technologies offer unprecedented perspectives for the rational investigation of complex biological systems. Indeed, omics present the ability of offering an extensive perception of the biochemistry and physiology of the cell and of any perturbing consequences of contaminants through the joint investigation of thousands of molecular responses simultaneously; then it has recently conducted to a fervent attention by research ecotoxicologists. Beyond the presentation of latest advances, exemplified here by omics investigation of cyanobacterial deleterious effects on various fishes (at various experimental and biological scales and with various analytical tools and pipeline), the present review paper re-explores the promising perspectives and also the pitfalls of such holistic investigations of the ecotoxicological response of organisms for environmental assessment.
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Affiliation(s)
- Benjamin Marie
- Muséum National d'Histoire Naturelle, UMR 7245, CNRS, MNHN Molécules de Communication et Adaptation des Micro-organismes (MCAM), équipe "Cyanobactéries, Cyanotoxines et Environnement", 12 rue Buffon, CP 39, 75231 Paris Cedex 05, France.
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35
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Tong L, Wu H, Wang MD. Integrating multi-omics data by learning modality invariant representations for improved prediction of overall survival of cancer. Methods 2020; 189:74-85. [PMID: 32763377 DOI: 10.1016/j.ymeth.2020.07.008] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 06/29/2020] [Accepted: 07/21/2020] [Indexed: 11/18/2022] Open
Abstract
Breast and ovarian cancers are the second and the fifth leading causes of cancer death among women. Predicting the overall survival of breast and ovarian cancer patients can facilitate the therapeutics evaluation and treatment decision making. Multi-scale multi-omics data such as gene expression, DNA methylation, miRNA expression, and copy number variations can provide insights on personalized survival. However, how to effectively integrate multi-omics data remains a challenging task. In this paper, we develop multi-omics integration methods to improve the prediction of overall survival for breast cancer and ovarian cancer patients. Because multi-omics data for the same patient jointly impact the survival of cancer patients, features from different -omics modality are related and can be modeled by either association or causal relationship (e.g., pathways). By extracting these relationships among modalities, we can get rid of the irrelevant information from high-throughput multi-omics data. However, it is infeasible to use the Brute Force method to capture all possible multi-omics interactions. Thus, we use deep neural networks with novel divergence-based consensus regularization to capture multi-omics interactions implicitly by extracting modality-invariant representations. In comparing the concatenation-based integration networks with our new divergence-based consensus networks, the breast cancer overall survival C-index is improved from 0.655±0.062 to 0.671±0.046 when combing DNA methylation and miRNA expression, and from 0.627±0.062 to 0.667±0.073 when combing miRNA expression and copy number variations. In summary, our novel deep consensus neural network has successfully improved the prediction of overall survival for breast cancer and ovarian cancer patients by implicitly learning the multi-omics interactions.
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Affiliation(s)
- Li Tong
- Dept. of Biomedical Engineering, Georgia Tech and Emory University, Atlanta, USA.
| | - Hang Wu
- Dept. of Biomedical Engineering, Georgia Tech, Atlanta, USA.
| | - May D Wang
- Dept. of Biomedical Engineering, Georgia Tech and Emory University, Atlanta, USA.
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36
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Randhawa V, Pathania S. Advancing from protein interactomes and gene co-expression networks towards multi-omics-based composite networks: approaches for predicting and extracting biological knowledge. Brief Funct Genomics 2020; 19:364-376. [PMID: 32678894 DOI: 10.1093/bfgp/elaa015] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 05/31/2020] [Accepted: 06/15/2020] [Indexed: 01/17/2023] Open
Abstract
Prediction of biological interaction networks from single-omics data has been extensively implemented to understand various aspects of biological systems. However, more recently, there is a growing interest in integrating multi-omics datasets for the prediction of interactomes that provide a global view of biological systems with higher descriptive capability, as compared to single omics. In this review, we have discussed various computational approaches implemented to infer and analyze two of the most important and well studied interactomes: protein-protein interaction networks and gene co-expression networks. We have explicitly focused on recent methods and pipelines implemented to infer and extract biologically important information from these interactomes, starting from utilizing single-omics data and then progressing towards multi-omics data. Accordingly, recent examples and case studies are also briefly discussed. Overall, this review will provide a proper understanding of the latest developments in protein and gene network modelling and will also help in extracting practical knowledge from them.
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Affiliation(s)
- Vinay Randhawa
- Department of Biochemistry, Panjab University, Chandigarh, 160014, India
| | - Shivalika Pathania
- Department of Biotechnology, Panjab University, Chandigarh, 160014, India
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37
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Cassotta M, Forbes-Hernández TY, Calderón Iglesias R, Ruiz R, Elexpuru Zabaleta M, Giampieri F, Battino M. Links between Nutrition, Infectious Diseases, and Microbiota: Emerging Technologies and Opportunities for Human-Focused Research. Nutrients 2020; 12:E1827. [PMID: 32575399 PMCID: PMC7353391 DOI: 10.3390/nu12061827] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 06/11/2020] [Accepted: 06/15/2020] [Indexed: 02/06/2023] Open
Abstract
The interaction between nutrition and human infectious diseases has always been recognized. With the emergence of molecular tools and post-genomics, high-resolution sequencing technologies, the gut microbiota has been emerging as a key moderator in the complex interplay between nutrients, human body, and infections. Much of the host-microbial and nutrition research is currently based on animals or simplistic in vitro models. Although traditional in vivo and in vitro models have helped to develop mechanistic hypotheses and assess the causality of the host-microbiota interactions, they often fail to faithfully recapitulate the complexity of the human nutrient-microbiome axis in gastrointestinal homeostasis and infections. Over the last decade, remarkable progress in tissue engineering, stem cell biology, microfluidics, sequencing technologies, and computing power has taken place, which has produced a new generation of human-focused, relevant, and predictive tools. These tools, which include patient-derived organoids, organs-on-a-chip, computational analyses, and models, together with multi-omics readouts, represent novel and exciting equipment to advance the research into microbiota, infectious diseases, and nutrition from a human-biology-based perspective. After considering some limitations of the conventional in vivo and in vitro approaches, in this review, we present the main novel available and emerging tools that are suitable for designing human-oriented research.
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Affiliation(s)
- Manuela Cassotta
- Centre for Nutrition and Health, Universidad Europea del Atlántico (UEA), 39001 Santander, Spain; (M.C.); (R.C.I.); (R.R.)
| | - Tamara Yuliett Forbes-Hernández
- Department of Analytical and Food Chemistry, Nutrition and Food Science Group, CITACA, CACTI, University of Vigo, 36310 Vigo, Spain;
| | - Ruben Calderón Iglesias
- Centre for Nutrition and Health, Universidad Europea del Atlántico (UEA), 39001 Santander, Spain; (M.C.); (R.C.I.); (R.R.)
| | - Roberto Ruiz
- Centre for Nutrition and Health, Universidad Europea del Atlántico (UEA), 39001 Santander, Spain; (M.C.); (R.C.I.); (R.R.)
| | - Maria Elexpuru Zabaleta
- Dipartimento di Scienze Cliniche e Molecolari, Facoltà di Medicina, Università Politecnica delle Marche, 60131 Ancona, Italy;
| | - Francesca Giampieri
- Department of Analytical and Food Chemistry, Nutrition and Food Science Group, CITACA, CACTI, University of Vigo, 36310 Vigo, Spain;
- Department of Clinical Sciences, Faculty of Medicine, Polytechnic University of Marche, 60131 Ancona, Italy
- College of Food Science and Technology, Northwest University, Xi’an 710069, China
| | - Maurizio Battino
- Department of Analytical and Food Chemistry, Nutrition and Food Science Group, CITACA, CACTI, University of Vigo, 36310 Vigo, Spain;
- Department of Clinical Sciences, Faculty of Medicine, Polytechnic University of Marche, 60131 Ancona, Italy
- International Research Center for Food Nutrition and Safety, Jiangsu University, Zhenjiang 212013, China
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38
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Eckhardt M, Hultquist JF, Kaake RM, Hüttenhain R, Krogan NJ. A systems approach to infectious disease. Nat Rev Genet 2020; 21:339-354. [PMID: 32060427 PMCID: PMC7839161 DOI: 10.1038/s41576-020-0212-5] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/08/2020] [Indexed: 01/01/2023]
Abstract
Ongoing social, political and ecological changes in the 21st century have placed more people at risk of life-threatening acute and chronic infections than ever before. The development of new diagnostic, prophylactic, therapeutic and curative strategies is critical to address this burden but is predicated on a detailed understanding of the immensely complex relationship between pathogens and their hosts. Traditional, reductionist approaches to investigate this dynamic often lack the scale and/or scope to faithfully model the dual and co-dependent nature of this relationship, limiting the success of translational efforts. With recent advances in large-scale, quantitative omics methods as well as in integrative analytical strategies, systems biology approaches for the study of infectious disease are quickly forming a new paradigm for how we understand and model host-pathogen relationships for translational applications. Here, we delineate a framework for a systems biology approach to infectious disease in three parts: discovery - the design, collection and analysis of omics data; representation - the iterative modelling, integration and visualization of complex data sets; and application - the interpretation and hypothesis-based inquiry towards translational outcomes.
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Affiliation(s)
- Manon Eckhardt
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA
- J. David Gladstone Institutes, San Francisco, CA, USA
| | - Judd F Hultquist
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA.
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA.
- J. David Gladstone Institutes, San Francisco, CA, USA.
- Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
| | - Robyn M Kaake
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA
- J. David Gladstone Institutes, San Francisco, CA, USA
| | - Ruth Hüttenhain
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA
- J. David Gladstone Institutes, San Francisco, CA, USA
| | - Nevan J Krogan
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA.
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA.
- J. David Gladstone Institutes, San Francisco, CA, USA.
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39
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Eetemadi A, Rai N, Pereira BMP, Kim M, Schmitz H, Tagkopoulos I. The Computational Diet: A Review of Computational Methods Across Diet, Microbiome, and Health. Front Microbiol 2020; 11:393. [PMID: 32318028 PMCID: PMC7146706 DOI: 10.3389/fmicb.2020.00393] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Accepted: 02/26/2020] [Indexed: 12/12/2022] Open
Abstract
Food and human health are inextricably linked. As such, revolutionary impacts on health have been derived from advances in the production and distribution of food relating to food safety and fortification with micronutrients. During the past two decades, it has become apparent that the human microbiome has the potential to modulate health, including in ways that may be related to diet and the composition of specific foods. Despite the excitement and potential surrounding this area, the complexity of the gut microbiome, the chemical composition of food, and their interplay in situ remains a daunting task to fully understand. However, recent advances in high-throughput sequencing, metabolomics profiling, compositional analysis of food, and the emergence of electronic health records provide new sources of data that can contribute to addressing this challenge. Computational science will play an essential role in this effort as it will provide the foundation to integrate these data layers and derive insights capable of revealing and understanding the complex interactions between diet, gut microbiome, and health. Here, we review the current knowledge on diet-health-gut microbiota, relevant data sources, bioinformatics tools, machine learning capabilities, as well as the intellectual property and legislative regulatory landscape. We provide guidance on employing machine learning and data analytics, identify gaps in current methods, and describe new scenarios to be unlocked in the next few years in the context of current knowledge.
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Affiliation(s)
- Ameen Eetemadi
- Department of Computer Science, University of California, Davis, Davis, CA, United States
- Genome Center, University of California, Davis, Davis, CA, United States
| | - Navneet Rai
- Genome Center, University of California, Davis, Davis, CA, United States
| | - Beatriz Merchel Piovesan Pereira
- Genome Center, University of California, Davis, Davis, CA, United States
- Department of Microbiology, University of California, Davis, Davis, CA, United States
| | - Minseung Kim
- Department of Computer Science, University of California, Davis, Davis, CA, United States
- Genome Center, University of California, Davis, Davis, CA, United States
- Process Integration and Predictive Analytics (PIPA LLC), Davis, CA, United States
| | - Harold Schmitz
- Graduate School of Management, University of California, Davis, Davis, CA, United States
| | - Ilias Tagkopoulos
- Department of Computer Science, University of California, Davis, Davis, CA, United States
- Genome Center, University of California, Davis, Davis, CA, United States
- Process Integration and Predictive Analytics (PIPA LLC), Davis, CA, United States
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40
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Seal DB, Das V, Goswami S, De RK. Estimating gene expression from DNA methylation and copy number variation: A deep learning regression model for multi-omics integration. Genomics 2020; 112:2833-2841. [PMID: 32234433 DOI: 10.1016/j.ygeno.2020.03.021] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 03/17/2020] [Accepted: 03/22/2020] [Indexed: 12/21/2022]
Abstract
Gene expression analysis plays a significant role for providing molecular insights in cancer. Various genetic and epigenetic factors (being dealt under multi-omics) affect gene expression giving rise to cancer phenotypes. A recent growth in understanding of multi-omics seems to provide a resource for integration in interdisciplinary biology since they altogether can draw the comprehensive picture of an organism's developmental and disease biology in cancers. Such large scale multi-omics data can be obtained from public consortium like The Cancer Genome Atlas (TCGA) and several other platforms. Integrating these multi-omics data from varied platforms is still challenging due to high noise and sensitivity of the platforms used. Currently, a robust integrative predictive model to estimate gene expression from these genetic and epigenetic data is lacking. In this study, we have developed a deep learning-based predictive model using Deep Denoising Auto-encoder (DDAE) and Multi-layer Perceptron (MLP) that can quantitatively capture how genetic and epigenetic alterations correlate with directionality of gene expression for liver hepatocellular carcinoma (LIHC). The DDAE used in the study has been trained to extract significant features from the input omics data to estimate the gene expression. These features have then been used for back-propagation learning by the multilayer perceptron for the task of regression and classification. We have benchmarked the proposed model against state-of-the-art regression models. Finally, the deep learning-based integration model has been evaluated for its disease classification capability, where an accuracy of 95.1% has been obtained.
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Affiliation(s)
- Dibyendu Bikash Seal
- A. K. Choudhury School of Information Technology, University of Calcutta, JD-2, Sector III, Salt Lake City, Kolkata 700106, India
| | - Vivek Das
- Novo Nordisk Research Center Seattle, Inc., 530 Fairview Ave N # 5000, Seattle, WA 98109, United States
| | - Saptarsi Goswami
- Bangabasi Morning College, 35 Rajkumar Chakraborty Sarani, Scott Ln, Kolkata 700009, India
| | - Rajat K De
- Machine Intelligence Unit, Indian Statistical Institute, 203 Barrackpore Trunk Road, Kolkata 700108, India.
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41
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Mallik S, Zhao Z. Graph- and rule-based learning algorithms: a comprehensive review of their applications for cancer type classification and prognosis using genomic data. Brief Bioinform 2020; 21:368-394. [PMID: 30649169 PMCID: PMC7373185 DOI: 10.1093/bib/bby120] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Revised: 10/26/2018] [Accepted: 11/21/2018] [Indexed: 12/20/2022] Open
Abstract
Cancer is well recognized as a complex disease with dysregulated molecular networks or modules. Graph- and rule-based analytics have been applied extensively for cancer classification as well as prognosis using large genomic and other data over the past decade. This article provides a comprehensive review of various graph- and rule-based machine learning algorithms that have been applied to numerous genomics data to determine the cancer-specific gene modules, identify gene signature-based classifiers and carry out other related objectives of potential therapeutic value. This review focuses mainly on the methodological design and features of these algorithms to facilitate the application of these graph- and rule-based analytical approaches for cancer classification and prognosis. Based on the type of data integration, we divided all the algorithms into three categories: model-based integration, pre-processing integration and post-processing integration. Each category is further divided into four sub-categories (supervised, unsupervised, semi-supervised and survival-driven learning analyses) based on learning style. Therefore, a total of 11 categories of methods are summarized with their inputs, objectives and description, advantages and potential limitations. Next, we briefly demonstrate well-known and most recently developed algorithms for each sub-category along with salient information, such as data profiles, statistical or feature selection methods and outputs. Finally, we summarize the appropriate use and efficiency of all categories of graph- and rule mining-based learning methods when input data and specific objective are given. This review aims to help readers to select and use the appropriate algorithms for cancer classification and prognosis study.
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Affiliation(s)
- Saurav Mallik
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center, Houston
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center, Houston
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42
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Csala A, Zwinderman AH, Hof MH. Multiset sparse partial least squares path modeling for high dimensional omics data analysis. BMC Bioinformatics 2020; 21:9. [PMID: 31918677 PMCID: PMC6953292 DOI: 10.1186/s12859-019-3286-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Accepted: 11/20/2019] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Recent technological developments have enabled the measurement of a plethora of biomolecular data from various omics domains, and research is ongoing on statistical methods to leverage these omics data to better model and understand biological pathways and genetic architectures of complex phenotypes. Current reviews report that the simultaneous analysis of multiple (i.e. three or more) high dimensional omics data sources is still challenging and suitable statistical methods are unavailable. Often mentioned challenges are the lack of accounting for the hierarchical structure between omics domains and the difficulty of interpretation of genomewide results. This study is motivated to address these challenges. We propose multiset sparse Partial Least Squares path modeling (msPLS), a generalized penalized form of Partial Least Squares path modeling, for the simultaneous modeling of biological pathways across multiple omics domains. msPLS simultaneously models the effect of multiple molecular markers, from multiple omics domains, on the variation of multiple phenotypic variables, while accounting for the relationships between data sources, and provides sparse results. The sparsity in the model helps to provide interpretable results from analyses of hundreds of thousands of biomolecular variables. RESULTS With simulation studies, we quantified the ability of msPLS to discover associated variables among high dimensional data sources. Furthermore, we analysed high dimensional omics datasets to explore biological pathways associated with Marfan syndrome and with Chronic Lymphocytic Leukaemia. Additionally, we compared the results of msPLS to the results of Multi-Omics Factor Analysis (MOFA), which is an alternative method to analyse this type of data. CONCLUSIONS msPLS is an multiset multivariate method for the integrative analysis of multiple high dimensional omics data sources. It accounts for the relationship between multiple high dimensional data sources while it provides interpretable results through its sparse solutions. The biomarkers found by msPLS in the omics datasets can be interpreted in terms of biological pathways associated with the pathophysiology of Marfan syndrome and of Chronic Lymphocytic Leukaemia. Additionally, msPLS outperforms MOFA in terms of variation explained in the chronic lymphocytic leukaemia dataset while it identifies the two most important clinical markers for Chronic Lymphocytic Leukaemia AVAILABILITY: http://uva.csala.me/mspls.https://github.com/acsala/2018_msPLS.
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Affiliation(s)
- Attila Csala
- Department of Clinical Epidemiology, Biostatistics and Bioinformatics, University of Amsterdam, Amsterdam, 1105 AZ The Netherlands
| | - Aeilko H. Zwinderman
- Department of Clinical Epidemiology, Biostatistics and Bioinformatics, University of Amsterdam, Amsterdam, 1105 AZ The Netherlands
| | - Michel H. Hof
- Department of Clinical Epidemiology, Biostatistics and Bioinformatics, University of Amsterdam, Amsterdam, 1105 AZ The Netherlands
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Network Aggregation to Enhance Results Derived from Multiple Analytics. IFIP ADVANCES IN INFORMATION AND COMMUNICATION TECHNOLOGY 2020. [PMCID: PMC7256384 DOI: 10.1007/978-3-030-49161-1_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The more complex data are, the higher the number of possibilities to extract partial information from those data. These possibilities arise by adopting different analytic approaches. The heterogeneity among these approaches and in particular the heterogeneity in results they produce are challenging for follow-up studies, including replication, validation and translational studies. Furthermore, they complicate the interpretation of findings with wide-spread relevance. Here, we take the example of statistical epistasis networks derived from genome-wide association studies with single nucleotide polymorphisms as nodes. Even though we are only dealing with a single data type, the epistasis detection problem suffers from many pitfalls, such as the wide variety of analytic tools to detect them, each highlighting different aspects of epistasis and exhibiting different properties in maintaining false positive control. To reconcile different network views to the same problem, we considered 3 network aggregation methods and discussed their performance in the context of epistasis network aggregation. We furthermore applied a latent class method as best performer to real-life data on inflammatory bowel disease (IBD) and highlighted its benefits to increase our understanding about IBD underlying genetic architectures.
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Cleves PA, Shumaker A, Lee J, Putnam HM, Bhattacharya D. Unknown to Known: Advancing Knowledge of Coral Gene Function. Trends Genet 2019; 36:93-104. [PMID: 31882190 DOI: 10.1016/j.tig.2019.11.001] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Revised: 10/31/2019] [Accepted: 11/06/2019] [Indexed: 12/18/2022]
Abstract
Given the catastrophic changes befalling coral reefs, understanding coral gene function is essential to advance reef conservation. This has proved challenging due to the paucity of genomic data and genetic tools available for corals. Recently, CRISPR/Cas9 gene editing was applied to these species; however, a major bottleneck is the identification and prioritization of candidate genes for manipulation. This issue is exacerbated by the many unknown ('dark') coral genes that may play key roles in the stress response. We review the use of gene coexpression networks that incorporate both known and unknown genes to identify targets for reverse genetic analysis. This approach also provides a framework for the annotation of dark genes in established interaction networks to improve our fundamental knowledge of coral gene function.
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Affiliation(s)
- Phillip A Cleves
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Alexander Shumaker
- Microbial Biology Graduate Program, Rutgers University, New Brunswick, NJ 08901, USA
| | - JunMo Lee
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ 08901, USA; Current address: Department of Oceanography, Kyungpook National University, Daegu 41566, Korea
| | - Hollie M Putnam
- Department of Biological Sciences, University of Rhode Island, Kingston, RI 02881, USA
| | - Debashish Bhattacharya
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ 08901, USA.
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Santibáñez R, Garrido D, Martin AJM. Pleione: A tool for statistical and multi-objective calibration of Rule-based models. Sci Rep 2019; 9:15104. [PMID: 31641245 PMCID: PMC6805871 DOI: 10.1038/s41598-019-51546-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2019] [Accepted: 09/24/2019] [Indexed: 11/17/2022] Open
Abstract
Mathematical models based on Ordinary Differential Equations (ODEs) are frequently used to describe and simulate biological systems. Nevertheless, such models are often difficult to understand. Unlike ODE models, Rule-Based Models (RBMs) utilise formal language to describe reactions as a cumulative number of statements that are easier to understand and correct. They are also gaining popularity because of their conciseness and simulation flexibility. However, RBMs generally lack tools to perform further analysis that requires simulation. This situation arises because exact and approximate simulations are computationally intensive. Translating RBMs into ODEs is commonly used to reduce simulation time, but this technique may be prohibitive due to combinatorial explosion. Here, we present the software called Pleione to calibrate RBMs. Parameter calibration is essential given the incomplete experimental determination of reaction rates and the goal of using models to reproduce experimental data. The software distributes stochastic simulations and calculations and incorporates equivalence tests to determine the fitness of RBMs compared with data. The primary features of Pleione were thoroughly tested on a model of gene regulation in Escherichia coli. Pleione yielded satisfactory results regarding calculation time and error reduction for multiple simulators, models, parameter search strategies, and computing infrastructures.
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Affiliation(s)
- Rodrigo Santibáñez
- Network Biology Lab, Centro de Genómica y Bioinformática, Facultad de Ciencias, Universidad Mayor, Santiago, 8580745, Chile
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, 7820436, Chile
| | - Daniel Garrido
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, 7820436, Chile
| | - Alberto J M Martin
- Network Biology Lab, Centro de Genómica y Bioinformática, Facultad de Ciencias, Universidad Mayor, Santiago, 8580745, Chile.
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Bro-Jørgensen J, Franks DW, Meise K. Linking behaviour to dynamics of populations and communities: application of novel approaches in behavioural ecology to conservation. Philos Trans R Soc Lond B Biol Sci 2019; 374:20190008. [PMID: 31352890 PMCID: PMC6710565 DOI: 10.1098/rstb.2019.0008] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/19/2019] [Indexed: 01/03/2023] Open
Abstract
The impact of environmental change on the reproduction and survival of wildlife is often behaviourally mediated, placing behavioural ecology in a central position to quantify population- and community-level consequences of anthropogenic threats to biodiversity. This theme issue demonstrates how recent conceptual and methodological advances in the discipline are applied to inform conservation. The issue highlights how the focus in behavioural ecology on understanding variation in behaviour between individuals, rather than just measuring the population mean, is critical to explaining demographic stochasticity and thereby reducing fuzziness of population models. The contributions also show the importance of knowing the mechanisms by which behaviour is achieved, i.e. the role of learning, reasoning and instincts, in order to understand how behaviours change in human-modified environments, where their function is less likely to be adaptive. More recent work has thus abandoned the 'adaptationist' paradigm of early behavioural ecology and increasingly measures evolutionary processes directly by quantifying selection gradients and phenotypic plasticity. To support quantitative predictions at the population and community levels, a rich arsenal of modelling techniques has developed, and interdisciplinary approaches show promising prospects for predicting the effectiveness of alternative management options, with the social sciences, movement ecology and epidemiology particularly pertinent. The theme issue furthermore explores the relevance of behaviour for global threat assessment, and practical advice is given as to how behavioural ecologists can augment their conservation impact by carefully selecting and promoting their study systems, and increasing their engagement with local communities, natural resource managers and policy-makers. Its aim to uncover the nuts and bolts of how natural systems work positions behavioural ecology squarely in the heart of conservation biology, where its perspective offers an all-important complement to more descriptive 'big-picture' approaches to priority setting. This article is part of the theme issue 'Linking behaviour to dynamics of populations and communities: application of novel approaches in behavioural ecology to conservation'.
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Affiliation(s)
- Jakob Bro-Jørgensen
- Mammalian Behaviour and Evolution Group, Department of Evolution, Ecology and Behaviour, University of Liverpool, Neston CH64 7TE, UK
| | - Daniel W. Franks
- Department of Biology, University of York, York YO10 5DD, UK
- Department of Computer Science, University of York, York YO10 5GH, UK
| | - Kristine Meise
- Mammalian Behaviour and Evolution Group, Department of Evolution, Ecology and Behaviour, University of Liverpool, Neston CH64 7TE, UK
- Department of Biology, University of York, York YO10 5DD, UK
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47
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Blencowe M, Arneson D, Ding J, Chen YW, Saleem Z, Yang X. Network modeling of single-cell omics data: challenges, opportunities, and progresses. Emerg Top Life Sci 2019; 3:379-398. [PMID: 32270049 PMCID: PMC7141415 DOI: 10.1042/etls20180176] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Revised: 06/07/2019] [Accepted: 06/24/2019] [Indexed: 01/07/2023]
Abstract
Single-cell multi-omics technologies are rapidly evolving, prompting both methodological advances and biological discoveries at an unprecedented speed. Gene regulatory network modeling has been used as a powerful approach to elucidate the complex molecular interactions underlying biological processes and systems, yet its application in single-cell omics data modeling has been met with unique challenges and opportunities. In this review, we discuss these challenges and opportunities, and offer an overview of the recent development of network modeling approaches designed to capture dynamic networks, within-cell networks, and cell-cell interaction or communication networks. Finally, we outline the remaining gaps in single-cell gene network modeling and the outlooks of the field moving forward.
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Affiliation(s)
- Montgomery Blencowe
- Department of Integrative Biology and Physiology, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, U.S.A
| | - Douglas Arneson
- Department of Integrative Biology and Physiology, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, U.S.A
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, U.S.A
| | - Jessica Ding
- Department of Integrative Biology and Physiology, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, U.S.A
| | - Yen-Wei Chen
- Department of Integrative Biology and Physiology, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, U.S.A
- Molecular Toxicology Program, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, U.S.A
| | - Zara Saleem
- Department of Integrative Biology and Physiology, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, U.S.A
| | - Xia Yang
- Department of Integrative Biology and Physiology, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, U.S.A
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, U.S.A
- Molecular Toxicology Program, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, U.S.A
- Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, U.S.A
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48
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Kim KJ, Tagkopoulos I. Application of machine learning in rheumatic disease research. Korean J Intern Med 2019; 34:708-722. [PMID: 30616329 PMCID: PMC6610179 DOI: 10.3904/kjim.2018.349] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/23/2018] [Accepted: 11/18/2018] [Indexed: 12/14/2022] Open
Abstract
Over the past decade, there has been a paradigm shift in how clinical data are collected, processed and utilized. Machine learning and artificial intelligence, fueled by breakthroughs in high-performance computing, data availability and algorithmic innovations, are paving the way to effective analyses of large, multi-dimensional collections of patient histories, laboratory results, treatments, and outcomes. In the new era of machine learning and predictive analytics, the impact on clinical decision-making in all clinical areas, including rheumatology, will be unprecedented. Here we provide a critical review of the machine-learning methods currently used in the analysis of clinical data, the advantages and limitations of these methods, and how they can be leveraged within the field of rheumatology.
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Affiliation(s)
- Ki-Jo Kim
- Division of Rheumatology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea
- Correspondence to Ki-Jo Kim, M.D. Division of Rheumatology, Department of Internal Medicine, College of Medicine, St. Vincent's Hospital, The Catholic University of Korea, 93 Jungbu-daero, Paldal-gu, Suwon 16247, Korea Tel: +82-31-249-8805 Fax: +82-31-253-8898 E-mail:
| | - Ilias Tagkopoulos
- Department of Computer Science, University of California, Davis, CA, USA
- Genome Center, University of California, Davis, CA, USA
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Haque S, Ahmad JS, Clark NM, Williams CM, Sozzani R. Computational prediction of gene regulatory networks in plant growth and development. CURRENT OPINION IN PLANT BIOLOGY 2019; 47:96-105. [PMID: 30445315 DOI: 10.1016/j.pbi.2018.10.005] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Revised: 10/05/2018] [Accepted: 10/18/2018] [Indexed: 05/22/2023]
Abstract
Plants integrate a wide range of cellular, developmental, and environmental signals to regulate complex patterns of gene expression. Recent advances in genomic technologies enable differential gene expression analysis at a systems level, allowing for improved inference of the network of regulatory interactions between genes. These gene regulatory networks, or GRNs, are used to visualize the causal regulatory relationships between regulators and their downstream target genes. Accordingly, these GRNs can represent spatial, temporal, and/or environmental regulations and can identify functional genes. This review summarizes recent computational approaches applied to different types of gene expression data to infer GRNs in the context of plant growth and development. Three stages of GRN inference are described: first, data collection and analysis based on the dataset type; second, network inference application based on data availability and proposed hypotheses; and third, validation based on in silico, in vivo, and in planta methods. In addition, this review relates data collection strategies to biological questions, organizes inference algorithms based on statistical methods and data types, discusses experimental design considerations, and provides guidelines for GRN inference with an emphasis on the benefits of integrative approaches, especially when a priori information is limited. Finally, this review concludes that computational frameworks integrating large-scale heterogeneous datasets are needed for a more accurate (e.g. fewer false interactions), detailed (e.g. discrimination between direct versus indirect interactions), and comprehensive (e.g. genetic regulation under various conditions and spatial locations) inference of GRNs.
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Affiliation(s)
- Samiul Haque
- Electrical and Computer Engineering, North Carolina State University, Raleigh, USA
| | - Jabeen S Ahmad
- Plant and Microbial Biology, North Carolina State University, Raleigh, USA
| | - Natalie M Clark
- Plant and Microbial Biology, North Carolina State University, Raleigh, USA
| | - Cranos M Williams
- Electrical and Computer Engineering, North Carolina State University, Raleigh, USA.
| | - Rosangela Sozzani
- Plant and Microbial Biology, North Carolina State University, Raleigh, USA.
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50
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Rai N, Huynh L, Kim M, Tagkopoulos I. Population collapse and adaptive rescue during long‐term chemostat fermentation. Biotechnol Bioeng 2019; 116:693-703. [DOI: 10.1002/bit.26898] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2018] [Revised: 11/02/2018] [Accepted: 12/06/2018] [Indexed: 11/09/2022]
Affiliation(s)
- Navneet Rai
- UC Davis Genome Center, University of California Davis California
- Department of Computer Science University of California Davis California
| | - Linh Huynh
- UC Davis Genome Center, University of California Davis California
- Department of Computer Science University of California Davis California
| | - Minseung Kim
- UC Davis Genome Center, University of California Davis California
- Department of Computer Science University of California Davis California
| | - Ilias Tagkopoulos
- UC Davis Genome Center, University of California Davis California
- Department of Computer Science University of California Davis California
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