{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T07:14:17Z","timestamp":1784013257612,"version":"3.55.0"},"reference-count":38,"publisher":"Oxford University Press (OUP)","issue":"Supplement_1","license":[{"start":{"date-parts":[[2021,7,12]],"date-time":"2021-07-12T00:00:00Z","timestamp":1626048000000},"content-version":"vor","delay-in-days":11,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61872094"],"award-info":[{"award-number":["61872094"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Municipal Science and Technology Major Project","award":["2018SHZDZX01"],"award-info":[{"award-number":["2018SHZDZX01"]}]},{"name":"Shanghai Center for BrainScience and Brain-Inspired Technology"},{"DOI":"10.13039\/501100013314","name":"111 Project","doi-asserted-by":"publisher","award":["B18015"],"award-info":[{"award-number":["B18015"]}],"id":[{"id":"10.13039\/501100013314","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Municipal Science and Technology Major Project","award":["2017SHZDZX01"],"award-info":[{"award-number":["2017SHZDZX01"]}]},{"DOI":"10.13039\/501100002341","name":"Academy of Finland","doi-asserted-by":"publisher","award":["315896"],"award-info":[{"award-number":["315896"]}],"id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001695","name":"JST","doi-asserted-by":"publisher","award":["JPMJAC1503"],"award-info":[{"award-number":["JPMJAC1503"]}],"id":[{"id":"10.13039\/501100001695","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NEXT","award":["19H04169"],"award-info":[{"award-number":["19H04169"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,8,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Automated function prediction (AFP) of proteins is a large-scale multi-label classification problem. Two limitations of most network-based methods for AFP are (i) a single model must be trained for each species and (ii) protein sequence information is totally ignored. These limitations cause weaker performance than sequence-based methods. Thus, the challenge is how to develop a powerful network-based method for AFP to overcome these limitations.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We propose DeepGraphGO, an end-to-end, multispecies graph neural network-based method for AFP, which makes the most of both protein sequence and high-order protein network information. Our multispecies strategy allows one single model to be trained for all species, indicating a larger number of training samples than existing methods. Extensive experiments with a large-scale dataset show that DeepGraphGO outperforms a number of competing state-of-the-art methods significantly, including DeepGOPlus and three representative network-based methods: GeneMANIA, deepNF and clusDCA. We further confirm the effectiveness of our multispecies strategy and the advantage of DeepGraphGO over so-called difficult proteins. Finally, we integrate DeepGraphGO into the state-of-the-art ensemble method, NetGO, as a component and achieve a further performance improvement.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>https:\/\/github.com\/yourh\/DeepGraphGO.<\/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\/btab270","type":"journal-article","created":{"date-parts":[[2021,4,23]],"date-time":"2021-04-23T00:04:18Z","timestamp":1619136258000},"page":"i262-i271","source":"Crossref","is-referenced-by-count":144,"title":["DeepGraphGO: graph neural network for large-scale, multispecies protein function prediction"],"prefix":"10.1093","volume":"37","author":[{"given":"Ronghui","family":"You","sequence":"first","affiliation":[{"name":"School of Computer Science, Fudan University , Shanghai 200433, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuwei","family":"Yao","sequence":"additional","affiliation":[{"name":"School of Computer Science, Fudan University , Shanghai 200433, 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, Japan"},{"name":"Department of Computer Science, Aalto University , Espoo, 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 Shanghai Institute of Artificial Intelligence Algorithms, Fudan University , Shanghai 200433, China"},{"name":"Ministry of Education, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University ), Shanghai 200433, China"},{"name":"Shanghai Key Lab of Intelligent Information Processing, Fudan University , Shanghai 200433, China"},{"name":"MOE Frontiers Center for Brain Science, Fudan University , Shanghai 200433, China"},{"name":"Zhangjiang Fudan International Innovation Center , Shanghai 200433, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,7,12]]},"reference":[{"key":"2023062410163631900_btab270-B1","article-title":"Understanding deep neural networks with rectified linear units","author":"Arora","year":"2018"},{"key":"2023062410163631900_btab270-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. 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