Multiview learning for understanding functional multiomics Nam D. Nguyen 1 , Daifeng Wang 2,3 1 Department of Computer Science, Stony Brook University, NY, USA 2 Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA 3 Waisman Center, University of Wisconsin-Madison, Madison, WI, USA Abstract The molecular mechanisms and functions in complex biological systems remain elusive. Recent high-throughput techniques 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 this, machine learning has been applied to analyze multiomics. This review introduces multiview learning—an emerging machine learning field—and envisions its potentially powerful applications to multiomics. Particularly, 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 including computer vision and speech recognition, multiview learning has not yet been widely applied to biological data—specifically, multiomics data. Therefore, we firstly review 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, aiming to discover the mechanistic interpretations across omics. Secondly, we explore possible applications to different biological systems, including human diseases, plants, and single-cell analysis, and discuss both the benefits and caveats of using multiview learning to discover the molecular mechanisms of these systems. Methods and Applications ) Multiomics data Multiview learning Multiview Empirical Risk Minimization (MV-ERM) Factorization-based versus alignment-based method References Nguyen, Nam D., and Daifeng Wang. "Multiview learning for understanding functional multiomics." PLOS Computational Biology 16.4 (2020): e1007677. Nguyen, Nam D., Ian K. Blaby, and Daifeng Wang. "ManiNetCluster: a novel manifold learning approach to reveal the functional links between gene networks." BMC genomics 20.12 (2019): 1-14. Gligorijević, Vladimir, and Nataša Pržulj. "Methods for biological data integration: perspectives and challenges." Journal of the Royal Society Interface 12.112 (2015): 20150571. [Gligorijević, Vladimir, and Nataša Pržulj. "Methods for biological data integration: perspectives and challenges." Journal of the Royal Society Interface 12.112 (2015): 20150571.] [Nguyen, Nam D., Ian K. Blaby, and Daifeng Wang. "ManiNetCluster: a novel manifold learning approach to reveal the functional links between gene networks." BMC genomics 20.12 (2019): 1-14.]

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Multiview learning for understanding functional multiomicsNam D. Nguyen1, Daifeng Wang2,3

1Department of Computer Science, Stony Brook University, NY, USA 2Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA

3Waisman Center, University of Wisconsin-Madison, Madison, WI, USA

Abstract

The molecular mechanisms and functions in complex biological systems remain elusive. Recent high-throughput techniques 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 this, machine learning has been applied to analyze multiomics. This review introduces multiview learning—an emerging machine learning field—and envisions its potentially powerful applications to multiomics. Particularly, 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 including computer vision and speech recognition, multiview learning has not yet been widely applied to biological data—specifically, multiomics data. Therefore, we firstly review 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, aiming to discover the mechanistic interpretations across omics. Secondly, we explore possible applications to different biological systems, including human diseases, plants, and single-cell analysis, and discuss both the benefits and caveats of using multiview learning to discover the molecular mechanisms of these systems.

Methods and Applications

)

Multiomics data

Multiview learning

Multiview Empirical Risk Minimization (MV-ERM)

Factorization-based versus alignment-based method

ReferencesNguyen, Nam D., and Daifeng Wang. "Multiview learning for understanding functional multiomics." PLOS Computational Biology 16.4 (2020): e1007677.

Nguyen, Nam D., Ian K. Blaby, and Daifeng Wang. "ManiNetCluster: a novel manifold learning approach to reveal the functional links between gene networks." BMC genomics 20.12 (2019): 1-14.

Gligorijević, Vladimir, and Nataša Pržulj. "Methods for biological data integration: perspectives and challenges." Journal of the Royal Society Interface 12.112 (2015): 20150571.

[Gligorijević, Vladimir, and Nataša Pržulj. "Methods for biological data integration: perspectives and challenges." Journal of the Royal Society Interface 12.112 (2015): 20150571.]

[Nguyen, Nam D., Ian K. Blaby, and Daifeng Wang. "ManiNetCluster: a novel manifold learning approach to reveal the functional links between gene networks." BMC genomics 20.12 (2019): 1-14.]