A Variational Information Bottleneck Approach to Multi-Omics Data Integration
- Authors
- Lee, Changhee; Van der Schaar, Mihaela
- Issue Date
- Feb-2021
- Publisher
- MICROTOME PUBLISHING
- Citation
- 24TH INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS (AISTATS), v.130
- Journal Title
- 24TH INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS (AISTATS)
- Volume
- 130
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/62572
- DOI
- 10.48550/arXiv.2102.03014
- ISSN
- 2640-3498
- 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 interview 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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Collections - College of Software > Department of Artificial Intelligence > 1. Journal Articles
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