Multimodal network diffusion predicts future disease–gene–chemical associations
Abstract
Precision medicine is an emerging field with hopes to improve patient treatment and reduce morbidity and mortality. To these ends, computational approaches have predicted associations among genes, chemicals and diseases. Such efforts, however, were often limited to using just some available association types. This lowers prediction coverage and, since prior evidence shows that integrating heterogeneous data is likely beneficial, it may limit accuracy. Therefore, we systematically tested whether using more association types improves prediction.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Oct 09, 2018
- Source ID
- 10.1093/bioinformatics/bty858
Entities
People
- Angela D. Wilkins
- Chih-hsu Lin
- Daniel M Konecki
- David F. Gleich
- Huda Nassar
- Meng Liu
- Olivier Lichtarge
- Stephen J. Wilson
Organizations
- Baylor College of Medicine
- Defense Advanced Research Projects Agency
- National Institutes of Health
- National Science Foundation
- Purdue University
- United States National Library of Medicine