{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T07:29:28Z","timestamp":1785569368661,"version":"3.56.0"},"reference-count":40,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2017,9,29]],"date-time":"2017-09-29T00:00:00Z","timestamp":1506643200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004052","name":"King Abdullah University of Science and Technology","doi-asserted-by":"publisher","award":["FCC\/1\/1976-08-01"],"award-info":[{"award-number":["FCC\/1\/1976-08-01"]}],"id":[{"id":"10.13039\/501100004052","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,2,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>A large number of protein sequences are becoming available through the application of novel high-throughput sequencing technologies. Experimental functional characterization of these proteins is time-consuming and expensive, and is often only done rigorously for few selected model organisms. Computational function prediction approaches have been suggested to fill this gap. The functions of proteins are classified using the Gene Ontology (GO), which contains over 40\u2009000 classes. Additionally, proteins have multiple functions, making function prediction a large-scale, multi-class, multi-label problem.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We have developed a novel method to predict protein function from sequence. We use deep learning to learn features from protein sequences as well as a cross-species protein\u2013protein interaction network. Our approach specifically outputs information in the structure of the GO and utilizes the dependencies between GO classes as background information to construct a deep learning model. We evaluate our method using the standards established by the Computational Assessment of Function Annotation (CAFA) and demonstrate a significant improvement over baseline methods such as BLAST, in particular for predicting cellular locations.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>Web server: http:\/\/deepgo.bio2vec.net, Source code: https:\/\/github.com\/bio-ontology-research-group\/deepgo<\/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\/btx624","type":"journal-article","created":{"date-parts":[[2017,9,27]],"date-time":"2017-09-27T11:10:10Z","timestamp":1506510610000},"page":"660-668","source":"Crossref","is-referenced-by-count":502,"title":["DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier"],"prefix":"10.1093","volume":"34","author":[{"given":"Maxat","family":"Kulmanov","sequence":"first","affiliation":[{"name":"Computer, Electrical and Mathematical Sciences & Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammed Asif","family":"Khan","sequence":"additional","affiliation":[{"name":"Computer, Electrical and Mathematical Sciences & Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8149-5890","authenticated-orcid":false,"given":"Robert","family":"Hoehndorf","sequence":"additional","affiliation":[{"name":"Computer, Electrical and Mathematical Sciences & Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2017,10,3]]},"reference":[{"key":"2023012712334050200_btx624-B1","author":"Abadi","year":"2016"},{"key":"2023012712334050200_btx624-B2","author":"Alberts","year":"2002"},{"key":"2023012712334050200_btx624-B3","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1038\/nbt.3300","article-title":"Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning","volume":"33","author":"Alipanahi","year":"2015","journal-title":"Nat. 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