Identifying antimicrobial peptides using word embedding with deep recurrent neural networks | Zendy

Md-Nafiz Hamid | Zendy; Iddo Friedberg | Zendy

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Identifying antimicrobial peptides using word embedding with deep recurrent neural networks

Author(s) -

Md-Nafiz Hamid,

Iddo Friedberg

Publication year - 2018

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bty937

Subject(s) - bacteriocin , antibiotic resistance , antimicrobial , antimicrobial peptides , computational biology , similarity (geometry) , computer science , artificial intelligence , word embedding , word (group theory) , biology , embedding , antibiotics , microbiology and biotechnology , mathematics , image (mathematics) , geometry

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.

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