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Ma W, Tang W, Kwok JS, Tong AH, Lo CW, Chu AT, Chung BH. A review on trends in development and translation of omics signatures in cancer. Comput Struct Biotechnol J 2024; 23:954-971. [PMID: 38385061 PMCID: PMC10879706 DOI: 10.1016/j.csbj.2024.01.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 01/31/2024] [Accepted: 01/31/2024] [Indexed: 02/23/2024] Open
Abstract
The field of cancer genomics and transcriptomics has evolved from targeted profiling to swift sequencing of individual tumor genome and transcriptome. The steady growth in genome, epigenome, and transcriptome datasets on a genome-wide scale has significantly increased our capability in capturing signatures that represent both the intrinsic and extrinsic biological features of tumors. These biological differences can help in precise molecular subtyping of cancer, predicting tumor progression, metastatic potential, and resistance to therapeutic agents. In this review, we summarized the current development of genomic, methylomic, transcriptomic, proteomic and metabolic signatures in the field of cancer research and highlighted their potentials in clinical applications to improve diagnosis, prognosis, and treatment decision in cancer patients.
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Affiliation(s)
- Wei Ma
- Hong Kong Genome Institute, Hong Kong, China
| | - Wenshu Tang
- Hong Kong Genome Institute, Hong Kong, China
| | | | | | | | | | - Brian H.Y. Chung
- Hong Kong Genome Institute, Hong Kong, China
- Department of Pediatrics and Adolescent Medicine, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Hong Kong Genome Project
- Hong Kong Genome Institute, Hong Kong, China
- Department of Pediatrics and Adolescent Medicine, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
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2
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Mukherjee A, Abraham S, Singh A, Balaji S, Mukunthan KS. From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies. Mol Biotechnol 2024:10.1007/s12033-024-01133-6. [PMID: 38565775 DOI: 10.1007/s12033-024-01133-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 02/27/2024] [Indexed: 04/04/2024]
Abstract
In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for drug discovery, is underpinned by big data, a transformative force in the current era. Omics data, characterized by its heterogeneity and enormity, has ushered biological and biomedical research into the big data domain. Acknowledging the significance of integrating diverse omics data strata, known as multi-omics studies, researchers delve into the intricate interrelationships among various omics layers. This review navigates the expansive omics landscape, showcasing tailored assays for each molecular layer through genomes to metabolomes. The sheer volume of data generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging as robust tools. These datasets not only refine disease classification but also enhance diagnostics and foster the development of targeted therapeutic strategies. Through the integration of high-throughput data, the review focuses on targeting and modeling multiple disease-regulated networks, validating interactions with multiple targets, and enhancing therapeutic potential using network pharmacology approaches. Ultimately, this exploration aims to illuminate the transformative impact of multi-omics in the big data era, shaping the future of biological research.
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Affiliation(s)
- Arnab Mukherjee
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - Suzanna Abraham
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - Akshita Singh
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - S Balaji
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - K S Mukunthan
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India.
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Jurenaite N, León-Periñán D, Donath V, Torge S, Jäkel R. SetQuence & SetOmic: Deep set transformers for whole genome and exome tumour analysis. Biosystems 2024; 235:105095. [PMID: 38065399 DOI: 10.1016/j.biosystems.2023.105095] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 10/17/2023] [Accepted: 11/28/2023] [Indexed: 12/21/2023]
Abstract
In oncology, Deep Learning has shown great potential to personalise tasks such as tumour type classification, based on per-patient omics data-sets. Being high dimensional, incorporation of such data in one model is a challenge, often leading to one-dimensional studies and, therefore, information loss. Instead, we first propose relying on non-fixed sets of whole genome or whole exome variant-associated sequences, which can be used for supervised learning of oncology-relevant tasks by our Set Transformer based Deep Neural Network, SetQuence. We optimise this architecture to improve its efficiency. This allows for exploration of not just coding but also non-coding variants, from large datasets. Second, we extend the model to incorporate these representations together with multiple other sources of omics data in a flexible way with SetOmic. Evaluation, using these representations, shows improved robustness and reduced information loss compared to previous approaches, while still being computationally tractable. By means of Explainable Artificial Intelligence methods, our models are able to recapitulate the biological contribution of highly attributed features in the tumours studied. This validation opens the door to novel directions in multi-faceted genome and exome wide biomarker discovery and personalised treatment among other presently clinically relevant tasks.
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Affiliation(s)
- Neringa Jurenaite
- Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), TU Dresden, Chemnitzer Str 46b, Dresden, 01187, Saxony, Germany.
| | - Daniel León-Periñán
- Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), TU Dresden, Chemnitzer Str 46b, Dresden, 01187, Saxony, Germany; Max-Delbrück-Centrum für Molekulare Medizin, Hannoversche Str. 28, Berlin, 10115, Germany.
| | - Veronika Donath
- Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), TU Dresden, Chemnitzer Str 46b, Dresden, 01187, Saxony, Germany.
| | - Sunna Torge
- Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), TU Dresden, Chemnitzer Str 46b, Dresden, 01187, Saxony, Germany.
| | - René Jäkel
- Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), TU Dresden, Chemnitzer Str 46b, Dresden, 01187, Saxony, Germany.
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Bailleux C, Chardin D, Guigonis JM, Ferrero JM, Chateau Y, Humbert O, Pourcher T, Gal J. Survival analysis of patient groups defined by unsupervised machine learning clustering methods based on patient metabolomic data. Comput Struct Biotechnol J 2023; 21:5136-5143. [PMID: 37920813 PMCID: PMC10618114 DOI: 10.1016/j.csbj.2023.10.033] [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: 04/30/2023] [Revised: 10/16/2023] [Accepted: 10/16/2023] [Indexed: 11/04/2023] Open
Abstract
Purpose Meta-analyses failed to accurately identify patients with non-metastatic breast cancer who are likely to benefit from chemotherapy, and metabolomics could provide new answers. In our previous published work, patients were clustered using five different unsupervised machine learning (ML) methods resulting in the identification of three clusters with distinct clinical and simulated survival data. The objective of this study was to evaluate the survival outcomes, with extended follow-up, using the same 5 different methods of unsupervised machine learning. Experimental design Forty-nine patients, diagnosed between 2013 and 2016, with non-metastatic BC were included retrospectively. Median follow-up was extended to 85.8 months. 449 metabolites were extracted from tumor resection samples by combined Liquid chromatography-mass spectrometry (LC-MS). Survival analyses were reported grouping together Cluster 1 and 2 versus cluster 3. Bootstrap optimization was applied. Results PCA k-means, K-sparse and Spectral clustering were the most effective methods to predict 2-year progression-free survival with bootstrap optimization (PFSb); as bootstrap example, with PCA k-means method, PFSb were 94% for cluster 1&2 versus 82% for cluster 3 (p = 0.01). PCA k-means method performed best, with higher reproducibility (mean HR=2 (95%CI [1.4-2.7]); probability of p ≤ 0.05 85%). Cancer-specific survival (CSS) and overall survival (OS) analyses highlighted a discrepancy between the 5 ML unsupervised methods. Conclusion Our study is a proof-of-principle that it is possible to use unsupervised ML methods on metabolomic data to predict PFS survival outcomes, with the best performance for PCA k-means. A larger population study is needed to draw conclusions from CSS and OS analyses.
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Affiliation(s)
- Caroline Bailleux
- University Côte d′Azur, Centre Antoine Lacassagne, Medical Oncology Department, Nice F-06189, France
- University Côte d′Azur, Commissariat à l′Energie Atomique et aux énergies alternatives, Institut Frédéric Joliot, Service Hospitalier Frédéric Joliot, laboratory Transporters in Oncology and Radiotherapy in Oncology (TIRO), School of medicine, Nice F-06100, France
| | - David Chardin
- University Côte d′Azur, Commissariat à l′Energie Atomique et aux énergies alternatives, Institut Frédéric Joliot, Service Hospitalier Frédéric Joliot, laboratory Transporters in Oncology and Radiotherapy in Oncology (TIRO), School of medicine, Nice F-06100, France
- University Côte d′Azur, Centre Antoine Lacassagne, Nuclear medicine Department, Nice F-06189, France
| | - Jean-Marie Guigonis
- University Côte d′Azur, Commissariat à l′Energie Atomique et aux énergies alternatives, Institut Frédéric Joliot, Service Hospitalier Frédéric Joliot, laboratory Transporters in Oncology and Radiotherapy in Oncology (TIRO), School of medicine, Nice F-06100, France
| | - Jean-Marc Ferrero
- University Côte d′Azur, Centre Antoine Lacassagne, Medical Oncology Department, Nice F-06189, France
| | - Yann Chateau
- University Côte d′Azur, Centre Antoine Lacassagne, Epidemiology and Biostatistics Department, Nice F-06189, France
| | - Olivier Humbert
- University Côte d′Azur, Commissariat à l′Energie Atomique et aux énergies alternatives, Institut Frédéric Joliot, Service Hospitalier Frédéric Joliot, laboratory Transporters in Oncology and Radiotherapy in Oncology (TIRO), School of medicine, Nice F-06100, France
- University Côte d′Azur, Centre Antoine Lacassagne, Nuclear medicine Department, Nice F-06189, France
| | - Thierry Pourcher
- University Côte d′Azur, Commissariat à l′Energie Atomique et aux énergies alternatives, Institut Frédéric Joliot, Service Hospitalier Frédéric Joliot, laboratory Transporters in Oncology and Radiotherapy in Oncology (TIRO), School of medicine, Nice F-06100, France
| | - Jocelyn Gal
- University Côte d′Azur, Centre Antoine Lacassagne, Epidemiology and Biostatistics Department, Nice F-06189, France
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Godlewski A, Czajkowski M, Mojsak P, Pienkowski T, Gosk W, Lyson T, Mariak Z, Reszec J, Kondraciuk M, Kaminski K, Kretowski M, Moniuszko M, Kretowski A, Ciborowski M. A comparison of different machine-learning techniques for the selection of a panel of metabolites allowing early detection of brain tumors. Sci Rep 2023; 13:11044. [PMID: 37422554 PMCID: PMC10329700 DOI: 10.1038/s41598-023-38243-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 07/05/2023] [Indexed: 07/10/2023] Open
Abstract
Metabolomics combined with machine learning methods (MLMs), is a powerful tool for searching novel diagnostic panels. This study was intended to use targeted plasma metabolomics and advanced MLMs to develop strategies for diagnosing brain tumors. Measurement of 188 metabolites was performed on plasma samples collected from 95 patients with gliomas (grade I-IV), 70 with meningioma, and 71 healthy individuals as a control group. Four predictive models to diagnose glioma were prepared using 10 MLMs and a conventional approach. Based on the cross-validation results of the created models, the F1-scores were calculated, then obtained values were compared. Subsequently, the best algorithm was applied to perform five comparisons involving gliomas, meningiomas, and controls. The best results were obtained using the newly developed hybrid evolutionary heterogeneous decision tree (EvoHDTree) algorithm, which was validated using Leave-One-Out Cross-Validation, resulting in an F1-score for all comparisons in the range of 0.476-0.948 and the area under the ROC curves ranging from 0.660 to 0.873. Brain tumor diagnostic panels were constructed with unique metabolites, which reduces the likelihood of misdiagnosis. This study proposes a novel interdisciplinary method for brain tumor diagnosis based on metabolomics and EvoHDTree, exhibiting significant predictive coefficients.
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Affiliation(s)
- Adrian Godlewski
- Clinical Research Centre, Medical University of Bialystok, M. Sklodowskiej-Curie 24a, 15-276, Białystok, Poland
| | - Marcin Czajkowski
- Faculty of Computer Science, Bialystok University of Technology, Białystok, Poland
| | - Patrycja Mojsak
- Clinical Research Centre, Medical University of Bialystok, M. Sklodowskiej-Curie 24a, 15-276, Białystok, Poland
| | - Tomasz Pienkowski
- Clinical Research Centre, Medical University of Bialystok, M. Sklodowskiej-Curie 24a, 15-276, Białystok, Poland
| | - Wioleta Gosk
- Clinical Research Centre, Medical University of Bialystok, M. Sklodowskiej-Curie 24a, 15-276, Białystok, Poland
| | - Tomasz Lyson
- Department of Neurosurgery, Medical University of Bialystok, Białystok, Poland
| | - Zenon Mariak
- Department of Neurosurgery, Medical University of Bialystok, Białystok, Poland
| | - Joanna Reszec
- Department of Medical Pathomorphology, Medical University of Bialystok, Białystok, Poland
| | - Marcin Kondraciuk
- Department of Population Medicine and Lifestyle Diseases Prevention, Medical University of Bialystok, Białystok, Poland
| | - Karol Kaminski
- Department of Population Medicine and Lifestyle Diseases Prevention, Medical University of Bialystok, Białystok, Poland
| | - Marek Kretowski
- Faculty of Computer Science, Bialystok University of Technology, Białystok, Poland
| | - Marcin Moniuszko
- Department of Regenerative Medicine and Immune Regulation, Medical University of Bialystok, Białystok, Poland
- Department of Allergology and Internal Medicine, Medical University of Bialystok, Białystok, Poland
| | - Adam Kretowski
- Clinical Research Centre, Medical University of Bialystok, M. Sklodowskiej-Curie 24a, 15-276, Białystok, Poland
- Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, Białystok, Poland
| | - Michal Ciborowski
- Clinical Research Centre, Medical University of Bialystok, M. Sklodowskiej-Curie 24a, 15-276, Białystok, Poland.
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Pandiyan S, Wang L. A comprehensive review on recent approaches for cancer drug discovery associated with artificial intelligence. Comput Biol Med 2022; 150:106140. [PMID: 36179510 DOI: 10.1016/j.compbiomed.2022.106140] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Revised: 07/20/2022] [Accepted: 09/18/2022] [Indexed: 11/03/2022]
Abstract
Through the revolutionization of artificial intelligence (AI) technologies in clinical research, significant improvement is observed in diagnosis of cancer. Utilization of these AI technologies, such as machine and deep learning, is imperative for the discovery of novel anticancer drugs and improves existing/ongoing cancer therapeutics. However, building a model for complicated cancers and their types remains a challenge due to lack of effective therapeutics that hinder the establishment of effective computational tools. In this review, we exploit recent approaches and state-of-the-art in implementing AI methods for anticancer drug discovery, and discussed how advances in these applications need to be considered in the current cancer therapeutics. Considering the immense potential of AI, we explore molecular docking and their interactions to recognize metabolic activities that support drug design. Finally, we highlight corresponding strategies in applying machine and deep learning methods to various types of cancer with their pros and cons.
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Affiliation(s)
- Sanjeevi Pandiyan
- Research Center for Intelligent Information Technology, Nantong University, Nantong, China; School of Information Science and Technology, Nantong University, Nantong, China; Nantong Research Institute for Advanced Communication Technologies, Nantong, China
| | - Li Wang
- Research Center for Intelligent Information Technology, Nantong University, Nantong, China; School of Information Science and Technology, Nantong University, Nantong, China; Nantong Research Institute for Advanced Communication Technologies, Nantong, China.
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Li H, Liu RB, Long CM, Teng Y, Cheng L, Liu Y. Development and Validation of a New Multiparametric Random Survival Forest Predictive Model for Breast Cancer Recurrence with a Potential Benefit to Individual Outcomes. Cancer Manag Res 2022; 14:909-923. [PMID: 35256862 PMCID: PMC8898179 DOI: 10.2147/cmar.s346871] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 01/27/2022] [Indexed: 12/24/2022] Open
Abstract
Purpose Breast cancer (BC) is a multi-factorial disease. Its individual prognosis varies; thus, individualized patient profiling is instrumental to improving BC management and individual outcomes. An economical, multiparametric, and practical model to predict BC recurrence is needed. Patients and Methods We retrospectively investigated the clinical data of BC patients treated at the Third Affiliated Hospital of Sun Yat-sen University and Liuzhou Women and Children’s Medical Center from January 2013 to December 2020. Random forest-recursive feature elimination (run by R caret package) was used to determine the best variable set, and the random survival forest method was used to develop a predictive model for BC recurrence. Results The training and validations sets included 623 and 151 patients, respectively. We selected 14 variables, the pathological (TNM) stage, gamma-glutamyl transpeptidase, total cholesterol, Ki-67, lymphocyte count, low-density lipoprotein, age, apolipoprotein B, high-density lipoprotein, globulin, neutrophil count to lymphocyte count ratio, alanine aminotransferase, triglyceride, and albumin to globulin ratio, using random survival forest (RSF)-recursive feature elimination. We developed a recurrence prediction model using RSF. Using area under the receiver operating characteristic curve and Kaplan–Meier survival analyses, the model performance was determined to be accurate. C-indexes were 0.997 and 0.936 for the training and validation sets, respectively. Conclusion The model could accurately predict BC recurrence. It aids clinicians in identifying high-risk patients and making treatment decisions for Breast cancer patients in China. This new multiparametric RSF model is instrumental for breast cancer recurrence prediction and potentially improves individual outcomes.
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Affiliation(s)
- Huan Li
- Department of Thyroid and Breast Surgery, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, People’s Republic of China
| | - Ren-Bin Liu
- Department of Thyroid and Breast Surgery, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, People’s Republic of China
| | - Chen-Meng Long
- Department of Breast Surgery, Liuzhou Women and Children’s Medical Center, Liuzhou, Guangxi, People’s Republic Of China
| | - Yuan Teng
- Department of Breast Surgery, Guangzhou Women and Children’s Medical Center, Guangzhou, Guangdong, People’s Republic of China
| | - Lin Cheng
- Department of Thyroid and Breast Surgery, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, People’s Republic of China
| | - Yu Liu
- Department of Thyroid and Breast Surgery, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, People’s Republic of China
- Correspondence: Yu Liu, Tel +8613560170809, Fax +86 20 85252154, Email
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Hatmal MM, Alshaer W, Mahmoud IS, Al-Hatamleh MAI, Al-Ameer HJ, Abuyaman O, Zihlif M, Mohamud R, Darras M, Al Shhab M, Abu-Raideh R, Ismail H, Al-Hamadi A, Abdelhay A. Investigating the association of CD36 gene polymorphisms (rs1761667 and rs1527483) with T2DM and dyslipidemia: Statistical analysis, machine learning based prediction, and meta-analysis. PLoS One 2021; 16:e0257857. [PMID: 34648514 PMCID: PMC8516279 DOI: 10.1371/journal.pone.0257857] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 09/11/2021] [Indexed: 12/15/2022] Open
Abstract
CD36 (cluster of differentiation 36) is a membrane protein involved in lipid metabolism and has been linked to pathological conditions associated with metabolic disorders, such as diabetes and dyslipidemia. A case-control study was conducted and included 177 patients with type-2 diabetes mellitus (T2DM) and 173 control subjects to study the involvement of CD36 gene rs1761667 (G>A) and rs1527483 (C>T) polymorphisms in the pathogenesis of T2DM and dyslipidemia among Jordanian population. Lipid profile, blood sugar, gender and age were measured and recorded. Also, genotyping analysis for both polymorphisms was performed. Following statistical analysis, 10 different neural networks and machine learning (ML) tools were used to predict subjects with diabetes or dyslipidemia. Towards further understanding of the role of CD36 protein and gene in T2DM and dyslipidemia, a protein-protein interaction network and meta-analysis were carried out. For both polymorphisms, the genotypic frequencies were not significantly different between the two groups (p > 0.05). On the other hand, some ML tools like multilayer perceptron gave high prediction accuracy (≥ 0.75) and Cohen's kappa (κ) (≥ 0.5). Interestingly, in K-star tool, the accuracy and Cohen's κ values were enhanced by including the genotyping results as inputs (0.73 and 0.46, respectively, compared to 0.67 and 0.34 without including them). This study confirmed, for the first time, that there is no association between CD36 polymorphisms and T2DM or dyslipidemia among Jordanian population. Prediction of T2DM and dyslipidemia, using these extensive ML tools and based on such input data, is a promising approach for developing diagnostic and prognostic prediction models for a wide spectrum of diseases, especially based on large medical databases.
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Affiliation(s)
- Ma’mon M. Hatmal
- Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, The Hashemite University, Zarqa, Jordan
- * E-mail:
| | - Walhan Alshaer
- Cell Therapy Centre, The University of Jordan, Amman, Jordan
| | - Ismail S. Mahmoud
- Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, The Hashemite University, Zarqa, Jordan
| | - Mohammad A. I. Al-Hatamleh
- Department of Immunology, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, Kelantan, Malaysia
| | - Hamzeh J. Al-Ameer
- Department of Biology and Biotechnology, American University of Madaba, Madaba, Jordan
- Department of Pharmacology, Faculty of Medicine, The University of Jordan, Amman, Jordan
| | - Omar Abuyaman
- Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, The Hashemite University, Zarqa, Jordan
| | - Malek Zihlif
- Department of Pharmacology, Faculty of Medicine, The University of Jordan, Amman, Jordan
| | - Rohimah Mohamud
- Department of Immunology, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, Kelantan, Malaysia
| | - Mais Darras
- Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, The Hashemite University, Zarqa, Jordan
| | - Mohammad Al Shhab
- Department of Pharmacology, Faculty of Medicine, The University of Jordan, Amman, Jordan
| | - Rand Abu-Raideh
- Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, The Hashemite University, Zarqa, Jordan
| | - Hilweh Ismail
- Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, The Hashemite University, Zarqa, Jordan
| | - Ali Al-Hamadi
- Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, The Hashemite University, Zarqa, Jordan
| | - Ali Abdelhay
- Department of Pharmacology, Faculty of Medicine, The University of Jordan, Amman, Jordan
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Phenomapping of Patients with Primary Breast Cancer Using Machine Learning-Based Unsupervised Cluster Analysis. J Pers Med 2021; 11:jpm11040272. [PMID: 33916398 PMCID: PMC8067194 DOI: 10.3390/jpm11040272] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Revised: 03/23/2021] [Accepted: 04/01/2021] [Indexed: 12/15/2022] Open
Abstract
Primary breast cancer (PBC) is a heterogeneous disease at the clinical, histopathological, and molecular levels. The improved classification of PBC might be important to identify subgroups of the disease, relevant to patient management. Machine learning algorithms may allow a better understanding of the relationships within heterogeneous clinical syndromes. This work aims to show the potential of unsupervised learning techniques for improving classification in PBC. A dataset of 712 women with PBC is used as a motivating example. A set of variables containing biological prognostic parameters is considered to define groups of individuals. Four different clustering methods are used: K-means, self-organising maps, hierarchical agglomerative (HAC), and Gaussian mixture models clustering. HAC outperforms the other clustering methods. With an optimal partitioning parameter, the methods identify two clusters with different clinical profiles. Patients in the first cluster are younger and have lower values of the oestrogen receptor (ER) and progesterone receptor (PgR) than patients in the second cluster. Moreover, cathepsin D values are lower in the first cluster. The three most important variables identified by the HAC are: age, ER, and PgR. Unsupervised learning seems a suitable alternative for the analysis of PBC data, opening up new perspectives in the particularly active domain of dissecting clinical heterogeneity.
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Lou SJ, Hou MF, Chang HT, Chiu CC, Lee HH, Yeh SCJ, Shi HY. Machine Learning Algorithms to Predict Recurrence within 10 Years after Breast Cancer Surgery: A Prospective Cohort Study. Cancers (Basel) 2020; 12:cancers12123817. [PMID: 33348826 PMCID: PMC7765963 DOI: 10.3390/cancers12123817] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 12/11/2020] [Accepted: 12/14/2020] [Indexed: 02/07/2023] Open
Abstract
No studies have discussed machine learning algorithms to predict recurrence within 10 years after breast cancer surgery. This study purposed to compare the accuracy of forecasting models to predict recurrence within 10 years after breast cancer surgery and to identify significant predictors of recurrence. Registry data for breast cancer surgery patients were allocated to a training dataset (n = 798) for model development, a testing dataset (n = 171) for internal validation, and a validating dataset (n = 171) for external validation. Global sensitivity analysis was then performed to evaluate the significance of the selected predictors. Demographic characteristics, clinical characteristics, quality of care, and preoperative quality of life were significantly associated with recurrence within 10 years after breast cancer surgery (p < 0.05). Artificial neural networks had the highest prediction performance indices. Additionally, the surgeon volume was the best predictor of recurrence within 10 years after breast cancer surgery, followed by hospital volume and tumor stage. Accurate recurrence within 10 years prediction by machine learning algorithms may improve precision in managing patients after breast cancer surgery and improve understanding of risk factors for recurrence within 10 years after breast cancer surgery.
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Affiliation(s)
- Shi-Jer Lou
- Graduate Institute of Technological and Vocational Education, National Pingtung University of Science and Technology, Pingtung 91201, Taiwan;
| | - Ming-Feng Hou
- College of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan;
- Department of Surgery, Kaohsiung Medical University Hospital, Kaohsiung 80756, Taiwan
| | - Hong-Tai Chang
- Department of Surgery, Kaohsiung Municipal United Hospital, Kaohsiung 80457, Taiwan;
| | - Chong-Chi Chiu
- Department of General Surgery, E-Da Cancer Hospital, I-Shou University, Kaohsiung 82445, Taiwan;
- School of Medicine, College of Medicine, I-Shou University, Kaohsiung 82445, Taiwan
| | - Hao-Hsien Lee
- Department of General Surgery, Chi Mei Medical Center, Liouying, Tainan 73657, Taiwan;
| | - Shu-Chuan Jennifer Yeh
- Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung 80708, Taiwan;
- Department of Business Management, National Sun Yat-sen University, Kaohsiung 80424, Taiwan
| | - Hon-Yi Shi
- Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung 80708, Taiwan;
- Department of Business Management, National Sun Yat-sen University, Kaohsiung 80424, Taiwan
- Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung 80708, Taiwan
- Department of Medical Research, China Medical University Hospital, China Medical University, Taichung 40402, Taiwan
- Correspondence: ; Tel.: +886-7-321-1101 (ext. 2648); Fax: +886-7-313-7487
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