期刊
BIOINFORMATICS
卷 34, 期 16, 页码 2740-2747出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/bty179
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资金
- National Science Foundation [1144106]
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1144106] Funding Source: National Science Foundation
Motivation: Bacterial resistance to antibiotics is a growing concern. Antimicrobial peptides (AMPs), natural components of innate immunity, are popular targets for developing new drugs. Machine learning methods are now commonly adopted by wet-laboratory researchers to screen for promising candidates. Results: In this work, we utilize deep learning to recognize antimicrobial activity. We propose a neural network model with convolutional and recurrent layers that leverage primary sequence composition. Results show that the proposed model outperforms state-of-the-art classification models on a comprehensive dataset. By utilizing the embedding weights, we also present a reduced-alphabet representation and show that reasonable AMP recognition can be maintained using nine amino acid types. Availability and implementation: Models and datasets are made freely available through the Antimicrobial Peptide Scanner vr.2 web server at www. ampscanner.com. Contact: amarda@gmu.edu (for general inquiries) or dan.veltri@gmail.com (for web server information) Supplementary information: Supplementary data are available at Bioinformatics online.
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