A Variational Information Bottleneck Approach to Multi-Omics Data Integration

• URL: http://arxiv.org/abs/2102.03014v1
• Date: Fri, 5 Feb 2021 06:05:39 GMT
• Title: A Variational Information Bottleneck Approach to Multi-Omics Data Integration
• Authors: Changhee Lee and Mihaela van der Schaar
• Abstract summary: We propose a deep variational information bottleneck (IB) approach for incomplete multi-view observations. Our method applies the IB framework on marginal and joint representations of the observed views to focus on intra-view and inter-view interactions that are relevant for the target. Experiments on real-world datasets show that our method consistently achieves gain from data integration and outperforms state-of-the-art benchmarks.
• Score: 98.6475134630792
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Integration of data from multiple omics techniques is becoming increasingly important in biomedical research. Due to non-uniformity and technical limitations in omics platforms, such integrative analyses on multiple omics, which we refer to as views, involve learning from incomplete observations with various view-missing patterns. This is challenging because i) complex interactions within and across observed views need to be properly addressed for optimal predictive power and ii) observations with various view-missing patterns need to be flexibly integrated. To address such challenges, we propose a deep variational information bottleneck (IB) approach for incomplete multi-view observations. Our method applies the IB framework on marginal and joint representations of the observed views to focus on intra-view and inter-view interactions that are relevant for the target. Most importantly, by modeling the joint representations as a product of marginal representations, we can efficiently learn from observed views with various view-missing patterns. Experiments on real-world datasets show that our method consistently achieves gain from data integration and outperforms state-of-the-art benchmarks.

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