{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T07:15:11Z","timestamp":1784013311461,"version":"3.55.0"},"reference-count":38,"publisher":"Oxford University Press (OUP)","issue":"Supplement_2","license":[{"start":{"date-parts":[[2024,9,4]],"date-time":"2024-09-04T00:00:00Z","timestamp":1725408000000},"content-version":"vor","delay-in-days":3,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62272105"],"award-info":[{"award-number":["62272105"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Municipal Science and Technology Major","award":["2018SHZDZX01"],"award-info":[{"award-number":["2018SHZDZX01"]}]},{"name":"ZJ Lab and Shanghai Center for Brain Science"},{"name":"Brain-Inspired Intelligence Technology","award":["B18015"],"award-info":[{"award-number":["B18015"]}]},{"name":"MEXT KAKENHI","award":["19H04169"],"award-info":[{"award-number":["19H04169"]}]},{"name":"MEXT KAKENHI","award":["20F20809"],"award-info":[{"award-number":["20F20809"]}]},{"name":"MEXT KAKENHI","award":["21H05027"],"award-info":[{"award-number":["21H05027"]}]},{"name":"MEXT KAKENHI","award":["22H03645"],"award-info":[{"award-number":["22H03645"]}]},{"DOI":"10.13039\/501100002341","name":"Academy of Finland","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Summary<\/jats:title>\n                  <jats:p>The vast majority of proteins still lack experimentally validated functional annotations, which highlights the importance of developing high-performance automated protein function prediction\/annotation (AFP) methods. While existing approaches focus on protein sequences, networks, and structural data, textual information related to proteins has been overlooked. However, roughly 82% of SwissProt proteins already possess literature information that experts have annotated. To efficiently and effectively use literature information, we present GORetriever, a two-stage deep information retrieval-based method for AFP. Given a target protein, in the first stage, candidate Gene Ontology (GO) terms are retrieved by using annotated proteins with similar descriptions. In the second stage, the GO terms are reranked based on semantic matching between the GO definitions and textual information (literature and protein description) of the target protein. Extensive experiments over benchmark datasets demonstrate the remarkable effectiveness of GORetriever in enhancing the AFP performance. Note that GORetriever is the key component of GOCurator, which has achieved first place in the latest critical assessment of protein function annotation (CAFA5: over 1600 teams participated), held in 2023\u20132024.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>GORetriever is publicly available at https:\/\/github.com\/ZhuLab-Fudan\/GORetriever.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae401","type":"journal-article","created":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T07:46:25Z","timestamp":1725522385000},"page":"ii53-ii61","source":"Crossref","is-referenced-by-count":17,"title":["GORetriever: reranking protein-description-based GO candidates by literature-driven deep information retrieval for protein function annotation"],"prefix":"10.1093","volume":"40","author":[{"given":"Huiying","family":"Yan","sequence":"first","affiliation":[{"name":"Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University , Shanghai 200433,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaojun","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University , Shanghai 200433,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hancheng","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University , Shanghai 200433,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiroshi","family":"Mamitsuka","sequence":"additional","affiliation":[{"name":"Bioinformatics Center, Institute for Chemical Research, Kyoto University , Uji, Kyoto Prefecture 611-0011,","place":["Japan"]},{"name":"Department of Computer Science, Aalto University , Espoo 00076,","place":["Finland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6067-5312","authenticated-orcid":false,"given":"Shanfeng","family":"Zhu","sequence":"additional","affiliation":[{"name":"Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University , Shanghai 200433,","place":["China"]},{"name":"Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education , Shanghai, 200433,","place":["China"]},{"name":"Shanghai Key Lab of Intelligent Information Processing and Shanghai Institute of Artificial Intelligence Algorithm, Fudan University , Shanghai, 200433,","place":["China"]},{"name":"Zhangjiang Fudan International Innovation Center , Shanghai, 200433,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,9,4]]},"reference":[{"key":"2024102913564776600_btae401-B1","doi-asserted-by":"crossref","first-page":"3389","DOI":"10.1093\/nar\/25.17.3389","article-title":"Gapped BLAST and PSI-BLAST: a new generation of protein database search programs","volume":"25","author":"Altschul","year":"1997","journal-title":"Nucleic Acids Res"},{"key":"2024102913564776600_btae401-B2","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1038\/75556","article-title":"Gene ontology: tool for the unification of biology","volume":"25","author":"Ashburner","year":"2000","journal-title":"Nat Genet"},{"key":"2024102913564776600_btae401-B3","author":"Bajaj","year":"2018"},{"key":"2024102913564776600_btae401-B4","doi-asserted-by":"crossref","first-page":"D523","DOI":"10.1093\/nar\/gkac1052","article-title":"UniProt: the universal protein knowledgebase in 2023","volume":"51","author":"Bateman","year":"2023","journal-title":"Nucleic Acids Res"},{"key":"2024102913564776600_btae401-B5","doi-asserted-by":"crossref","first-page":"i318","DOI":"10.1093\/bioinformatics\/btad208","article-title":"Combining protein sequences and structures with transformers and equivariant graph neural networks to predict protein function","volume":"39","author":"Boadu","year":"2023","journal-title":"Bioinformatics"},{"key":"2024102913564776600_btae401-B6","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1007\/978-1-4939-3167-5_2","article-title":"UniProtKB\/Swiss-Prot, the manually annotated section of the uniprot knowledgebase: how to use the entry view","volume":"1374","author":"Boutet","year":"2016","journal-title":"Plant Bioinf Methods Protoc"},{"key":"2024102913564776600_btae401-B7","doi-asserted-by":"crossref","first-page":"2825","DOI":"10.1093\/bioinformatics\/btab198","article-title":"TALE: transformer-based protein function annotation with joint sequence\u2013label embedding","volume":"37","author":"Cao","year":"2021","journal-title":"Bioinformatics"},{"key":"2024102913564776600_btae401-B8","doi-asserted-by":"crossref","first-page":"i53","DOI":"10.1093\/bioinformatics\/btt228","article-title":"Information-theoretic evaluation of predicted ontological annotations","volume":"29","author":"Clark","year":"2013","journal-title":"Bioinformatics"},{"key":"2024102913564776600_btae401-B9","author":"Friedberg","year":"2023"},{"key":"2024102913564776600_btae401-B10","author":"Gane","year":"2022"},{"key":"2024102913564776600_btae401-B11","doi-asserted-by":"crossref","first-page":"3168","DOI":"10.1038\/s41467-021-23303-9","article-title":"Structure-based protein function prediction using graph convolutional networks","volume":"12","author":"Gligorijevi\u0107","year":"2021","journal-title":"Nat Commun"},{"key":"2024102913564776600_btae401-B12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3458754","article-title":"Domain-specific language model pretraining for biomedical natural language processing","volume":"3","author":"Gu","year":"2021","journal-title":"ACM Trans Comput Healthcare"},{"key":"2024102913564776600_btae401-B13","doi-asserted-by":"crossref","first-page":"D1057","DOI":"10.1093\/nar\/gku1113","article-title":"The Goa database: gene ontology annotation updates for 2015","volume":"43","author":"Huntley","year":"2015","journal-title":"Nucleic Acids Res"},{"key":"2024102913564776600_btae401-B14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-016-1037-6","article-title":"An expanded evaluation of protein function prediction methods shows an improvement in accuracy","volume":"17","author":"Jiang","year":"2016","journal-title":"Genome Biol"},{"key":"2024102913564776600_btae401-B15","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1093\/bioinformatics\/btz595","article-title":"DeepGOPlus: improved protein function prediction from sequence","volume":"36","author":"Kulmanov","year":"2020","journal-title":"Bioinformatics"},{"key":"2024102913564776600_btae401-B16","doi-asserted-by":"crossref","first-page":"i238","DOI":"10.1093\/bioinformatics\/btac256","article-title":"DeepGOZero: improving protein function prediction from sequence and zero-shot learning based on ontology axioms","volume":"38","author":"Kulmanov","year":"2022","journal-title":"Bioinformatics"},{"key":"2024102913564776600_btae401-B17","doi-asserted-by":"crossref","first-page":"bbab502","DOI":"10.1093\/bib\/bbab502","article-title":"Accurate protein function prediction via graph attention networks with predicted structure information","volume":"23","author":"Lai","year":"2021","journal-title":"Brief Bioinf"},{"key":"2024102913564776600_btae401-B18","first-page":"1188","volume-title":"ICML","author":"Le","year":"2014"},{"key":"2024102913564776600_btae401-B19","first-page":"2356","volume-title":"SIGIR","author":"Lin","year":"2021"},{"key":"2024102913564776600_btae401-B20","doi-asserted-by":"crossref","first-page":"1264","DOI":"10.3390\/genes11111264","article-title":"Automatic gene function prediction in the 2020\u2019s","volume":"11","author":"Makrodimitris","year":"2020","journal-title":"Genes (Basel)"},{"key":"2024102913564776600_btae401-B21","doi-asserted-by":"crossref","first-page":"D222","DOI":"10.1093\/nar\/gku1221","article-title":"CDD: NCBI\u2019s conserved domain database","volume":"43","author":"Marchler-Bauer","year":"2015","journal-title":"Nucleic Acids Res"},{"key":"2024102913564776600_btae401-B22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3439726","article-title":"Deep learning\u2013based text classification: a comprehensive review","volume":"54","author":"Minaee","year":"2021","journal-title":"ACM Comput Surv"},{"key":"2024102913564776600_btae401-B23","first-page":"708","volume-title":"EMNLP 2020","author":"Nogueira","year":"2020"},{"key":"2024102913564776600_btae401-B24","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1038\/nmeth.2340","article-title":"A large-scale evaluation of computational protein function prediction","volume":"10","author":"Radivojac","year":"2013","journal-title":"Nat Methods"},{"key":"2024102913564776600_btae401-B25","first-page":"9689","author":"Rao","year":"2019"},{"key":"2024102913564776600_btae401-B26","first-page":"3982","volume-title":"EMNLP-IJCNLP 2019","author":"Reimers","year":"2019"},{"key":"2024102913564776600_btae401-B27","doi-asserted-by":"crossref","first-page":"e2016239118","DOI":"10.1073\/pnas.2016239118","article-title":"Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences","volume":"118","year":"2021","journal-title":"Proc Natl Acad Sci USA"},{"key":"2024102913564776600_btae401-B28","doi-asserted-by":"crossref","first-page":"D376","DOI":"10.1093\/nar\/gku947","article-title":"CATH: comprehensive structural and functional annotations for genome sequences","volume":"43","author":"Sillitoe","year":"2015","journal-title":"Nucleic Acids Res"},{"key":"2024102913564776600_btae401-B29","first-page":"349","article-title":"NetGO 3.0: a protein language model improves large-scale functional annotations. Genomics","volume":"21","author":"Wang","year":"2023","journal-title":"Proteomics Bioinf"},{"key":"2024102913564776600_btae401-B30","first-page":"279","volume-title":"RECOMB","author":"Xu","year":"2022"},{"key":"2024102913564776600_btae401-B31","first-page":"38749","volume-title":"ICML","author":"Xu","year":"2023"},{"key":"2024102913564776600_btae401-B32","doi-asserted-by":"crossref","first-page":"W469","DOI":"10.1093\/nar\/gkab398","article-title":"NetGO 2.0: improving large-scale protein function prediction with massive sequence, text, domain, family and network information","volume":"49","author":"Yao","year":"2021","journal-title":"Nucleic Acids Res"},{"key":"2024102913564776600_btae401-B33","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.ymeth.2018.05.026","article-title":"DeepText2GO: improving large-scale protein function prediction with deep semantic text representation","volume":"145","author":"You","year":"2018","journal-title":"Methods"},{"key":"2024102913564776600_btae401-B34","doi-asserted-by":"crossref","first-page":"2465","DOI":"10.1093\/bioinformatics\/bty130","article-title":"GOLabeler: improving sequence-based large-scale protein function prediction by learning to rank","volume":"34","author":"You","year":"2018","journal-title":"Bioinformatics"},{"key":"2024102913564776600_btae401-B35","doi-asserted-by":"crossref","first-page":"W379","DOI":"10.1093\/nar\/gkz388","article-title":"NetGO: improving large-scale protein function prediction with massive network information","volume":"47","author":"You","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2024102913564776600_btae401-B36","doi-asserted-by":"crossref","first-page":"i262","DOI":"10.1093\/bioinformatics\/btab270","article-title":"DeepGraphGO: graph neural network for large-scale, multispecies protein function prediction","volume":"37","author":"You","year":"2021","journal-title":"Bioinformatics"},{"key":"2024102913564776600_btae401-B37","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1186\/s13059-019-1835-8","article-title":"The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens","volume":"20","author":"Zhou","year":"2019","journal-title":"Genome Biol"},{"key":"2024102913564776600_btae401-B38","doi-asserted-by":"crossref","first-page":"e1010793","DOI":"10.1371\/journal.pcbi.1010793","article-title":"Integrating unsupervised language model with triplet neural networks for protein gene ontology prediction","volume":"18","author":"Zhu","year":"2022","journal-title":"PLoS Comput Biol"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/40\/Supplement_2\/ii53\/60177512\/btae401.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/40\/Supplement_2\/ii53\/60177512\/btae401.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T13:57:13Z","timestamp":1730210233000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/40\/Supplement_2\/ii53\/7749084"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,1]]},"references-count":38,"journal-issue":{"issue":"Supplement_2","published-print":{"date-parts":[[2024,9,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btae401","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2024,9]]},"published":{"date-parts":[[2024,9,1]]}}}