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Title: BayReL: Bayesian Relational Learning for Multi-omics Data Integration
High-throughput molecular profiling technologies have produced high-dimensional multi-omics data, enabling systematic understanding of living systems at the genome scale. Studying molecular interactions across different data types helps reveal signal transduction mechanisms across different classes of molecules. In this paper, we develop a novel Bayesian representation learning method that infers the relational interactions across multi-omics data types. Our method, Bayesian Relational Learning (BayReL) for multi-omics data integration, takes advantage of a priori known relationships among the same class of molecules, modeled as a graph at each corresponding view, to learn view-specific latent variables as well as a multi-partite graph that encodes the interactions across views. Our experiments on several real-world datasets demonstrate enhanced performance of BayReL in inferring meaningful interactions compared to existing baselines.  more » « less
Award ID(s):
1839816 1934904 1848596
NSF-PAR ID:
10209353
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
Advances in neural information processing systems
Volume:
33
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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    Results

    We developed a supervised variational Bayesian factor model that extracts multi-omics signatures from high-throughput profiling datasets that can span multiple data types. Signature-based multiPle-omics intEgration via lAtent factoRs (SPEAR) adaptively determines factor rank, emphasis on factor structure, data relevance and feature sparsity. The method improves the reconstruction of underlying factors in synthetic examples and prediction accuracy of coronavirus disease 2019 severity and breast cancer tumor subtypes.

    Availability and implementation

    SPEAR is a publicly available R-package hosted at https://bitbucket.org/kleinstein/SPEAR.

     
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