{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T22:37:33Z","timestamp":1776465453983,"version":"3.51.2"},"reference-count":58,"publisher":"Oxford University Press (OUP)","issue":"16","license":[{"start":{"date-parts":[[2018,3,24]],"date-time":"2018-03-24T00:00:00Z","timestamp":1521849600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1144106"],"award-info":[{"award-number":["1144106"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,8,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>Models and datasets are made freely available through the Antimicrobial Peptide Scanner vr.2 web server at www.ampscanner.com.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/bty179","type":"journal-article","created":{"date-parts":[[2018,3,23]],"date-time":"2018-03-23T04:10:48Z","timestamp":1521778248000},"page":"2740-2747","source":"Crossref","is-referenced-by-count":513,"title":["Deep learning improves antimicrobial peptide recognition"],"prefix":"10.1093","volume":"34","author":[{"given":"Daniel","family":"Veltri","sequence":"first","affiliation":[{"name":"Bioinformatics and Computational Biosciences Branch, Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, U.S. National Institutes of Health, Rockville, MD, USA"},{"name":"Medical Science & Computing, LLC, Rockville, MD, USA"}]},{"given":"Uday","family":"Kamath","sequence":"additional","affiliation":[{"name":"Digital Reasoning, McLean, VA, USA"}]},{"given":"Amarda","family":"Shehu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, George Mason University, Fairfax, VA, USA"},{"name":"Department of Bioengineering, George Mason University, Fairfax, VA, USA"},{"name":"School of Systems Biology, George Mason University, Manassas, VA, USA"}]}],"member":"286","published-online":{"date-parts":[[2018,3,24]]},"reference":[{"key":"2023012712590862200_bty179-B1","volume-title":"Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI)","author":"Abadi","year":"2016"},{"key":"2023012712590862200_bty179-B2","author":"Bahdanau","year":"2014"},{"key":"2023012712590862200_bty179-B3","first-page":"291","volume-title":"Bioinformatics for Geneticists","author":"Betts","year":"2003"},{"key":"2023012712590862200_bty179-B4","doi-asserted-by":"crossref","first-page":"e0117394.","DOI":"10.1371\/journal.pone.0117394","article-title":"Bioprospecting the american alligator (Alligator mississippiensis) host defense peptidome","volume":"10","author":"Bishop","year":"2015","journal-title":"PLoS ONE"},{"key":"2023012712590862200_bty179-B5","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1046\/j.1365-2796.2003.01228.x","article-title":"Antibacterial peptides: basic facts and emerging concepts","volume":"254","author":"Boman","year":"2003","journal-title":"J. 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