Incomplete multi‐modal learning of omics data for phenotype prediction and biomarker discovery
The proposed method effectively integrates multi-omics data, even with incomplete modalities. By rediscovering biomarkers aligned with other studies, it demonstrates the potential of deep learning approaches in multi-omics research for AD.
Authors
- Elissa J. Chesler (ORCID: https://orcid.org/0000-0002-5642-5062)
- Daniel M. Gatti (ORCID: https://orcid.org/0000-0003-0667-9926)
- S. J. Park
- AR Dunn
- Kyungwook Lee
- Doyeong Hwang (ORCID: https://orcid.org/0000-0002-1452-9023)
- Soorin Yim
- Kiyoung Kim
- Kristen MS O'Connell
Institutions
- LG (United States) (US)
- Jackson Laboratory (US)
Publication Details
- Journal
- Alzheimer s & Dementia
- Published
- 2025-12-01
- DOI
- https://doi.org/10.1002/alz70855_100741
- Primary Topic
- Bioinformatics and Genomic Networks
- Type
- article
- Field-Weighted Citation Impact
- 0.00