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Multiview learning for understanding functional multiomics
- Source :
- PLoS Computational Biology, PLoS Computational Biology, Vol 16, Iss 4, p e1007677 (2020)
- Publication Year :
- 2020
- Publisher :
- Public Library of Science, 2020.
-
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.
- Subjects :
- 0301 basic medicine
Proteomics
Computer science
Gene Expression
Review
Biochemistry
Machine Learning
Database and Informatics Methods
0302 clinical medicine
Cluster Analysis
Biology (General)
DNA methylation
Ecology
Applied Mathematics
Simulation and Modeling
Brain
High-Throughput Nucleotide Sequencing
Genomics
Chromatin
Variety (cybernetics)
Learning data
Nucleic acids
Identification (information)
Computational Theory and Mathematics
Modeling and Simulation
Data Interpretation, Statistical
Physical Sciences
Epigenetics
Single-Cell Analysis
DNA modification
Sequence Analysis
Transcriptome Analysis
Algorithms
Chromatin modification
Chromosome biology
Computer and Information Sciences
Cell biology
Proteomics methods
QH301-705.5
Bioinformatics
Research and Analysis Methods
03 medical and health sciences
Cellular and Molecular Neuroscience
Machine Learning Algorithms
Artificial Intelligence
Alzheimer Disease
Genetics
Humans
Metabolomics
Empirical risk minimization
Non-coding RNA
Molecular Biology
Ecology, Evolution, Behavior and Systematics
Natural antisense transcripts
Data interpretation
Biology and Life Sciences
Computational Biology
DNA
Genome Analysis
Data science
Gene regulation
MicroRNAs
030104 developmental biology
RNA
Sequence Alignment
030217 neurology & neurosurgery
Multiview learning
Mathematics
Chlamydomonas reinhardtii
Software
Subjects
Details
- Language :
- English
- ISSN :
- 15537358 and 1553734X
- Volume :
- 16
- Issue :
- 4
- Database :
- OpenAIRE
- Journal :
- PLoS Computational Biology
- Accession number :
- edsair.doi.dedup.....4027a88df79ed011f8438766759d37ef