Identifying antimicrobial peptides using word embedding with deep recurrent neural networks
Abstract
Antibiotic resistance constitutes a major public health crisis, and finding new sources of antimicrobial drugs is crucial to solving it. Bacteriocins, which are bacterially produced antimicrobial peptide products, are candidates for broadening the available choices of antimicrobials. However, the discovery of new bacteriocins by genomic mining is hampered by their sequences’ low complexity and high variance, which frustrates sequence similarity-based searches.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Nov 10, 2018
- Source ID
- 10.1093/bioinformatics/bty937
Entities
People
- Iddo Friedberg
- Md-nafiz Hamid
Organizations
- Army Research Office
- Intelligence Advanced Research Projects Activity
- Iowa State University
- National Science Foundation
- Office of the Director of National Intelligence