Integrating multimodal data through interpretable heterogeneous ensembles
Abstract
Integrating multimodal data represents an effective approach to predicting biomedical characteristics, such as protein functions and disease outcomes. However, existing data integration approaches do not sufficiently address the heterogeneous semantics of multimodal data. In particular, early and intermediate approaches that rely on a uniform integrated representation reinforce the consensus among the modalities but may lose exclusive local information. The alternative late integration approach that can address this challenge has not been systematically studied for biomedical problems.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 01, 2022
- Source ID
- 10.1093/bioadv/vbac065
Entities
People
- Gaurav Pandey
- Jeffrey N Law
- Linhua Wang
- T M Murali
- Yan Chak Li
Organizations
- Baylor College of Medicine
- Icahn School of Medicine at Mount Sinai
- National Institutes of Health
- National Renewable Energy Laboratory
- Virginia Tech