A Variational Information Bottleneck Approach to Multi-Omics Data Integration
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Changhee | - |
dc.contributor.author | Van der Schaar, Mihaela | - |
dc.date.accessioned | 2023-03-08T11:12:10Z | - |
dc.date.available | 2023-03-08T11:12:10Z | - |
dc.date.issued | 2021-02 | - |
dc.identifier.issn | 2640-3498 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/62572 | - |
dc.description.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. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | MICROTOME PUBLISHING | - |
dc.title | A Variational Information Bottleneck Approach to Multi-Omics Data Integration | - |
dc.type | Article | - |
dc.identifier.doi | 10.48550/arXiv.2102.03014 | - |
dc.identifier.bibliographicCitation | 24TH INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS (AISTATS), v.130 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000659893801082 | - |
dc.citation.title | 24TH INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS (AISTATS) | - |
dc.citation.volume | 130 | - |
dc.type.docType | Proceedings Paper | - |
dc.publisher.location | 미국 | - |
dc.subject.keywordPlus | SETS | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Mathematics, Applied | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.description.journalRegisteredClass | foreign | - |
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