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Theron D, Hopkins LN, Sutherland HG, Griffiths LR, Fernandez F. Can Genetic Markers Predict the Sporadic Form of Alzheimer's Disease? An Updated Review on Genetic Peripheral Markers. Int J Mol Sci 2023; 24:13480. [PMID: 37686283 PMCID: PMC10488021 DOI: 10.3390/ijms241713480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 08/22/2023] [Accepted: 08/25/2023] [Indexed: 09/10/2023] Open
Abstract
Alzheimer's disease (AD) is the most common form of dementia that affects millions of individuals worldwide. Although the research over the last decades has provided new insight into AD pathophysiology, there is currently no cure for the disease. AD is often only diagnosed once the symptoms have become prominent, particularly in the late-onset (sporadic) form of AD. Consequently, it is essential to further new avenues for early diagnosis. With recent advances in genomic analysis and a lower cost of use, the exploration of genetic markers alongside RNA molecules can offer a key avenue for early diagnosis. We have here provided a brief overview of potential genetic markers differentially expressed in peripheral tissues in AD cases compared to controls, as well as considering the changes to the dynamics of RNA molecules. By integrating both genotype and RNA changes reported in AD, biomarker profiling can be key for developing reliable AD diagnostic tools.
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Affiliation(s)
- Danelda Theron
- School of Behavioural and Health Sciences, Faculty of Heath Sciences, Australian Catholic University, Banyo, QLD 4014, Australia;
- Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology, 60 Musk Ave, Kelvin Grove, QLD 4059, Australia; (L.N.H.); (H.G.S.); (L.R.G.)
| | - Lloyd N. Hopkins
- Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology, 60 Musk Ave, Kelvin Grove, QLD 4059, Australia; (L.N.H.); (H.G.S.); (L.R.G.)
| | - Heidi G. Sutherland
- Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology, 60 Musk Ave, Kelvin Grove, QLD 4059, Australia; (L.N.H.); (H.G.S.); (L.R.G.)
| | - Lyn R. Griffiths
- Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology, 60 Musk Ave, Kelvin Grove, QLD 4059, Australia; (L.N.H.); (H.G.S.); (L.R.G.)
| | - Francesca Fernandez
- School of Behavioural and Health Sciences, Faculty of Heath Sciences, Australian Catholic University, Banyo, QLD 4014, Australia;
- Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology, 60 Musk Ave, Kelvin Grove, QLD 4059, Australia; (L.N.H.); (H.G.S.); (L.R.G.)
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Young-Pearse TL, Lee H, Hsieh YC, Chou V, Selkoe DJ. Moving beyond amyloid and tau to capture the biological heterogeneity of Alzheimer's disease. Trends Neurosci 2023; 46:426-444. [PMID: 37019812 PMCID: PMC10192069 DOI: 10.1016/j.tins.2023.03.005] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 02/28/2023] [Accepted: 03/09/2023] [Indexed: 04/05/2023]
Abstract
Alzheimer's disease (AD) manifests along a spectrum of cognitive deficits and levels of neuropathology. Genetic studies support a heterogeneous disease mechanism, with around 70 associated loci to date, implicating several biological processes that mediate risk for AD. Despite this heterogeneity, most experimental systems for testing new therapeutics are not designed to capture the genetically complex drivers of AD risk. In this review, we first provide an overview of those aspects of AD that are largely stereotyped and those that are heterogeneous, and we review the evidence supporting the concept that different subtypes of AD are important to consider in the design of agents for the prevention and treatment of the disease. We then dive into the multifaceted biological domains implicated to date in AD risk, highlighting studies of the diverse genetic drivers of disease. Finally, we explore recent efforts to identify biological subtypes of AD, with an emphasis on the experimental systems and data sets available to support progress in this area.
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Affiliation(s)
- Tracy L Young-Pearse
- Ann Romney Center for Neurologic Diseases, Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
| | - Hyo Lee
- Ann Romney Center for Neurologic Diseases, Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Yi-Chen Hsieh
- Ann Romney Center for Neurologic Diseases, Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Vicky Chou
- Ann Romney Center for Neurologic Diseases, Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Dennis J Selkoe
- Ann Romney Center for Neurologic Diseases, Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
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Iturria-Medina Y, Adewale Q, Khan AF, Ducharme S, Rosa-Neto P, O’Donnell K, Petyuk VA, Gauthier S, De Jager PL, Breitner J, Bennett DA. Unified epigenomic, transcriptomic, proteomic, and metabolomic taxonomy of Alzheimer's disease progression and heterogeneity. SCIENCE ADVANCES 2022; 8:eabo6764. [PMID: 36399579 PMCID: PMC9674284 DOI: 10.1126/sciadv.abo6764] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 09/30/2022] [Indexed: 06/16/2023]
Abstract
Alzheimer's disease (AD) is a heterogeneous disorder with abnormalities in multiple biological domains. In an advanced machine learning analysis of postmortem brain and in vivo blood multi-omics molecular data (N = 1863), we integrated epigenomic, transcriptomic, proteomic, and metabolomic profiles into a multilevel biological AD taxonomy. We obtained a personalized multilevel molecular index of AD dementia progression that predicts severity of neuropathologies, and identified three robust molecular-based subtypes that explain much of the pathologic and clinical heterogeneity of AD. These subtypes present distinct patterns of alteration in DNA methylation, RNA, proteins, and metabolites, identifiable in the brain and subsequently in blood. In addition, the genetic variations that predispose to the various AD subtypes in brain predict distinct spatial patterns of alteration in cell types, suggesting a unique influence of each putative AD variant on neuropathological mechanisms. These observations support that an individually tailored multi-omics molecular taxonomy of AD may represent distinct targets for preventive or treatment interventions.
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Affiliation(s)
- Yasser Iturria-Medina
- Neurology and Neurosurgery Department, Montreal Neurological Institute, Montreal, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Canada
- Ludmer Centre for Neuroinformatics and Mental Health, Montreal, Canada
| | - Quadri Adewale
- Neurology and Neurosurgery Department, Montreal Neurological Institute, Montreal, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Canada
- Ludmer Centre for Neuroinformatics and Mental Health, Montreal, Canada
| | - Ahmed F. Khan
- Neurology and Neurosurgery Department, Montreal Neurological Institute, Montreal, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Canada
- Ludmer Centre for Neuroinformatics and Mental Health, Montreal, Canada
| | - Simon Ducharme
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Canada
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Canada
| | - Pedro Rosa-Neto
- McGill University Research Centre for Studies in Aging, Douglas Research Centre, Montreal, Canada
| | - Kieran O’Donnell
- Ludmer Centre for Neuroinformatics and Mental Health, Montreal, Canada
- Yale School of Medicine, New Haven, CT 06519, USA
| | - Vladislav A. Petyuk
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Serge Gauthier
- McGill University Research Centre for Studies in Aging, Douglas Research Centre, Montreal, Canada
| | - Philip L. De Jager
- Center for Translational and Computational Neuroimmunology, Department of Neurology and Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - John Breitner
- Centre for Studies on Prevention of Alzheimer’s Disease (StoP-AD), Douglas Research Centre, Montreal, Canada
- Department of Psychiatry, McGill University, Montreal, Canada
| | - David A. Bennett
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
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4
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Ahmed H, Soliman H, Elmogy M. Early detection of Alzheimer's disease using single nucleotide polymorphisms analysis based on gradient boosting tree. Comput Biol Med 2022; 146:105622. [PMID: 35751201 DOI: 10.1016/j.compbiomed.2022.105622] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 03/25/2022] [Accepted: 03/29/2022] [Indexed: 11/18/2022]
Abstract
Alzheimer's disease (AD) is a degenerative disorder that attacks nerve cells in the brain. AD leads to memory loss and cognitive & intellectual impairments that can influence social activities and decision-making. The most common type of human genetic variation is single nucleotide polymorphisms (SNPs). SNPs are beneficial markers of complex gene-disease. Many common and serious diseases, such as AD, have associated SNPs. Detection of SNP biomarkers linked with AD could help in the early prediction and diagnosis of this disease. The main objective of this paper is to predict and diagnose AD based on SNPs biomarkers with high classification accuracy in the early stages. One of the most concerning problems is the high number of features. Thus, the paper proposes a comprehensive framework for early AD detection and detecting the most significant genes based on SNPs analysis. Usage of machine learning (ML) techniques to identify new biomarkers of AD is also suggested. In the proposed system, two feature selection techniques are separately checked: the information gain filter and Boruta wrapper. The two feature selection techniques were used to select the most significant genes related to AD in this system. Filter methods measure the relevance of features by their correlation with dependent variables, while wrapper methods measure the usefulness of a subset of features by training a model on it. Gradient boosting tree (GBT) has been applied on all AD genetic data of neuroimaging initiative phase 1 (ADNI-1) and Whole-Genome Sequencing (WGS) datasets by using two feature selection techniques. In the whole-genome approach ADNI-1, results revealed that the GBT learning algorithm scored an overall accuracy of 99.06% in the case of using Boruta feature selection. Using information gain feature selection, the proposed system achieved an average accuracy of 94.87%. The results show that the proposed system is preferable for the early detection of AD. Also, the results revealed that the Boruta wrapper feature selection is superior to the information gain filter technique.
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Affiliation(s)
- Hala Ahmed
- Information Technology Dept., Faculty of Computers and Information, Mansoura University, Mansoura, P.O.35516, Egypt
| | - Hassan Soliman
- Information Technology Dept., Faculty of Computers and Information, Mansoura University, Mansoura, P.O.35516, Egypt
| | - Mohammed Elmogy
- Information Technology Dept., Faculty of Computers and Information, Mansoura University, Mansoura, P.O.35516, Egypt.
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Mishra R, Li B. The Application of Artificial Intelligence in the Genetic Study of Alzheimer's Disease. Aging Dis 2020; 11:1567-1584. [PMID: 33269107 PMCID: PMC7673858 DOI: 10.14336/ad.2020.0312] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Accepted: 03/12/2020] [Indexed: 12/13/2022] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease in which genetic factors contribute approximately 70% of etiological effects. Studies have found many significant genetic and environmental factors, but the pathogenesis of AD is still unclear. With the application of microarray and next-generation sequencing technologies, research using genetic data has shown explosive growth. In addition to conventional statistical methods for the processing of these data, artificial intelligence (AI) technology shows obvious advantages in analyzing such complex projects. This article first briefly reviews the application of AI technology in medicine and the current status of genetic research in AD. Then, a comprehensive review is focused on the application of AI in the genetic research of AD, including the diagnosis and prognosis of AD based on genetic data, the analysis of genetic variation, gene expression profile, gene-gene interaction in AD, and genetic analysis of AD based on a knowledge base. Although many studies have yielded some meaningful results, they are still in a preliminary stage. The main shortcomings include the limitations of the databases, failing to take advantage of AI to conduct a systematic biology analysis of multilevel databases, and lack of a theoretical framework for the analysis results. Finally, we outlook the direction of future development. It is crucial to develop high quality, comprehensive, large sample size, data sharing resources; a multi-level system biology AI analysis strategy is one of the development directions, and computational creativity may play a role in theory model building, verification, and designing new intervention protocols for AD.
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Affiliation(s)
- Rohan Mishra
- Washington Institute for Health Sciences, Arlington, VA 22203, USA
| | - Bin Li
- Washington Institute for Health Sciences, Arlington, VA 22203, USA
- Georgetown University Medical Center, Washington D.C. 20057, USA
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Spanos F, Liddelow SA. An Overview of Astrocyte Responses in Genetically Induced Alzheimer's Disease Mouse Models. Cells 2020; 9:E2415. [PMID: 33158189 PMCID: PMC7694249 DOI: 10.3390/cells9112415] [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: 08/26/2020] [Revised: 10/26/2020] [Accepted: 11/02/2020] [Indexed: 12/21/2022] Open
Abstract
Alzheimer's disease (AD) is the most common form of dementia. Despite many years of intense research, there is currently still no effective treatment. Multiple cell types contribute to disease pathogenesis, with an increasing body of data pointing to the active participation of astrocytes. Astrocytes play a pivotal role in the physiology and metabolic functions of neurons and other cells in the central nervous system. Because of their interactions with other cell types, astrocyte functions must be understood in their biologic context, thus many studies have used mouse models, of which there are over 190 available for AD research. However, none appear able to fully recapitulate the many functional changes in astrocytes reported in human AD brains. Our review summarizes the observations of astrocyte biology noted in mouse models of familial and sporadic AD. The limitations of AD mouse models will be discussed and current attempts to overcome these disadvantages will be described. With increasing understanding of the non-neuronal contributions to disease, the development of new methods and models will provide further insights and address important questions regarding the roles of astrocytes and other non-neuronal cells in AD pathophysiology. The next decade will prove to be full of exciting opportunities to address this devastating disease.
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Affiliation(s)
- Fokion Spanos
- Neuroscience Institute, NYU Grossman School of Medicine, New York, NY 10016, USA;
| | - Shane A. Liddelow
- Neuroscience Institute, NYU Grossman School of Medicine, New York, NY 10016, USA;
- Department of Neuroscience and Physiology, NYU Grossman School of Medicine, New York, NY 10016, USA
- Department of Ophthalmology, NYU Grossman School of Medicine, New York, NY 10016, USA
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Morabito S, Miyoshi E, Michael N, Swarup V. Integrative genomics approach identifies conserved transcriptomic networks in Alzheimer's disease. Hum Mol Genet 2020; 29:2899-2919. [PMID: 32803238 PMCID: PMC7566321 DOI: 10.1093/hmg/ddaa182] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 07/10/2020] [Accepted: 07/27/2020] [Indexed: 12/19/2022] Open
Abstract
Alzheimer's disease (AD) is a devastating neurological disorder characterized by changes in cell-type proportions and consequently marked alterations of the transcriptome. Here we use a data-driven systems biology meta-analytical approach across three human AD cohorts, encompassing six cortical brain regions, and integrate with multi-scale datasets comprising of DNA methylation, histone acetylation, transcriptome- and genome-wide association studies and quantitative trait loci to further characterize the genetic architecture of AD. We perform co-expression network analysis across more than 1200 human brain samples, identifying robust AD-associated dysregulation of the transcriptome, unaltered in normal human aging. We assess the cell-type specificity of AD gene co-expression changes and estimate cell-type proportion changes in human AD by integrating co-expression modules with single-cell transcriptome data generated from 27 321 nuclei from human postmortem prefrontal cortical tissue. We also show that genetic variants of AD are enriched in a microglial AD-associated module and identify key transcription factors regulating co-expressed modules. Additionally, we validate our results in multiple published human AD gene expression datasets, which can be easily accessed using our online resource (https://swaruplab.bio.uci.edu/consensusAD).
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Affiliation(s)
- Samuel Morabito
- Mathematical, Computational and Systems Biology (MCSB) Program, University of California, Irvine, CA 92697, USA
- Institute for Memory Impairments and Neurological Disorders (MIND), University of California, Irvine, CA 92697, USA
| | - Emily Miyoshi
- Department of Neurobiology and Behavior, University of California, Irvine, CA 92697, USA
- Institute for Memory Impairments and Neurological Disorders (MIND), University of California, Irvine, CA 92697, USA
| | - Neethu Michael
- Department of Neurobiology and Behavior, University of California, Irvine, CA 92697, USA
- Institute for Memory Impairments and Neurological Disorders (MIND), University of California, Irvine, CA 92697, USA
| | - Vivek Swarup
- Department of Neurobiology and Behavior, University of California, Irvine, CA 92697, USA
- Institute for Memory Impairments and Neurological Disorders (MIND), University of California, Irvine, CA 92697, USA
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De Cesco S, Davis JB, Brennan PE. TargetDB: A target information aggregation tool and tractability predictor. PLoS One 2020; 15:e0232644. [PMID: 32877430 PMCID: PMC7467329 DOI: 10.1371/journal.pone.0232644] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Accepted: 07/28/2020] [Indexed: 12/15/2022] Open
Abstract
When trying to identify new potential therapeutic protein targets, access to data and knowledge is increasingly important. In a field where new resources and data sources become available every day, it is crucial to be able to take a step back and look at the wider picture in order to identify potential drug targets. While this task is routinely performed by bespoke literature searches, it is often time-consuming and lacks uniformity when comparing multiple targets at one time. To address this challenge, we developed TargetDB, a tool that aggregates public information available on given target(s) (links to disease, safety, 3D structures, ligandability, novelty, etc.) and assembles it in an easy to read output ready for the researcher to analyze. In addition, we developed a target scoring system based on the desirable attributes of good therapeutic targets and machine learning classification system to categorize novel targets as having promising or challenging tractrability. In this manuscript, we present the methodology used to develop TargetDB as well as test cases.
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Affiliation(s)
- Stephane De Cesco
- Nuffield Department of Medicine, ARUK Oxford Drug Discovery Institute, Target Discovery Institute, University of Oxford, Oxford, United-Kingdom
- * E-mail: (PEB); (SDC)
| | - John B. Davis
- Nuffield Department of Medicine, ARUK Oxford Drug Discovery Institute, Target Discovery Institute, University of Oxford, Oxford, United-Kingdom
| | - Paul E. Brennan
- Nuffield Department of Medicine, ARUK Oxford Drug Discovery Institute, Target Discovery Institute, University of Oxford, Oxford, United-Kingdom
- * E-mail: (PEB); (SDC)
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Rangaswamy U, Dharshini SAP, Yesudhas D, Gromiha MM. VEPAD - Predicting the effect of variants associated with Alzheimer's disease using machine learning. Comput Biol Med 2020; 124:103933. [PMID: 32828070 DOI: 10.1016/j.compbiomed.2020.103933] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 07/25/2020] [Accepted: 07/25/2020] [Indexed: 12/26/2022]
Abstract
INTRODUCTION Alzheimer's disease (AD) is a complex and heterogeneous disease that affects neuronal cells over time and it is prevalent among all neurodegenerative diseases. Next Generation Sequencing (NGS) techniques are widely used for developing high-throughput screening methods to identify biomarkers and variants, which help early diagnosis and treatments. OBJECTIVE The primary purpose of this study is to develop a classification model using machine learning for predicting the deleterious effect of variants with respect to AD. METHODS We have constructed a set of 20,401 deleterious and 37,452 control variants from Genome-Wide Association Study (GWAS) and Genotype-Tissue Expression (GTEx) portals, respectively. Recursive feature elimination using cross-validation (RFECV) followed by a forward feature selection method was utilized to select the important features and a random forest classifier was used for distinguishing between deleterious and neutral variants. RESULTS Our method showed an accuracy of 81.21% on 10-fold cross-validation and 70.63% on a test set of 5785 variants. The same test set was used to compare the performance of CADD and FATHMM and their accuracies are in the range of 54%-62%. CONCLUSION Our model is freely available as the Variant Effect Predictor for Alzheimer's Disease (VEPAD) at http://web.iitm.ac.in/bioinfo2/vepad/. VEPAD can be used to predict the effect of new variants associated with AD.
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Affiliation(s)
- Uday Rangaswamy
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, 600036, India
| | - S Akila Parvathy Dharshini
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, 600036, India
| | - Dhanusha Yesudhas
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, 600036, India
| | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, 600036, India; School of Computing, Tokyo Tech World Research Hub Initiative (WRHI), Institute of Innovative Research, Tokyo Institute of Technology, Midori-ku, Kanagawa, 226-8503, Yokohama, Japan.
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Abstract
The molecular mechanisms and functions in complex biological systems currently remain elusive. Recent high-throughput techniques, such as next-generation sequencing, have generated a wide variety of multiomics datasets that enable the identification of biological functions and mechanisms via multiple facets. However, integrating these large-scale multiomics data and discovering functional insights are, nevertheless, challenging tasks. To address these challenges, machine learning has been broadly applied to analyze multiomics. This review introduces multiview learning-an emerging machine learning field-and envisions its potentially powerful applications to multiomics. In particular, multiview learning is more effective than previous integrative methods for learning data's heterogeneity and revealing cross-talk patterns. Although it has been applied to various contexts, such as computer vision and speech recognition, multiview learning has not yet been widely applied to biological data-specifically, multiomics data. Therefore, this paper firstly reviews recent multiview learning methods and unifies them in a framework called multiview empirical risk minimization (MV-ERM). We further discuss the potential applications of each method to multiomics, including genomics, transcriptomics, and epigenomics, in an aim to discover the functional and mechanistic interpretations across omics. Secondly, we explore possible applications to different biological systems, including human diseases (e.g., brain disorders and cancers), plants, and single-cell analysis, and discuss both the benefits and caveats of using multiview learning to discover the molecular mechanisms and functions of these systems.
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Affiliation(s)
- Nam D. Nguyen
- Department of Computer Science, Stony Brook University, Stony Brook, New York, United States of America
| | - Daifeng Wang
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Waisman Center, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
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