{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T15:32:14Z","timestamp":1776094334328,"version":"3.50.1"},"reference-count":32,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2023,1,24]],"date-time":"2023-01-24T00:00:00Z","timestamp":1674518400000},"content-version":"vor","delay-in-days":0,"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":["12171434"],"award-info":[{"award-number":["12171434"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U22A20102"],"award-info":[{"award-number":["U22A20102"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,2,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>Protein\u2013protein interaction (PPI) networks and transcriptional regulatory networks are critical in regulating cells and their signaling. A thorough understanding of PPIs can provide more insights into cellular physiology at normal and disease states. Although numerous methods have been proposed to predict PPIs, it is still challenging for interaction prediction between unknown proteins. In this study, a novel neural network named AFTGAN was constructed to predict multi-type PPIs. Regarding feature input, ESM-1b embedding containing much biological information for proteins was added as a protein sequence feature besides amino acid co-occurrence similarity and one-hot coding. An ensemble network was also constructed based on a transformer encoder containing an AFT module (performing the weight operation on vital protein sequence feature information) and graph attention network (extracting the relational features of protein pairs) for the part of the network framework.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The experimental results showed that the Micro-F1 of the AFTGAN based on three partitioning schemes (BFS, DFS and the random mode) on the SHS27K and SHS148K datasets was 0.685, 0.711 and 0.867, as well as 0.745, 0.819 and 0.920, respectively, all higher than that of other popular methods. In addition, the experimental comparisons confirmed the performance superiority of the proposed model for predicting PPIs of unknown proteins on the STRING dataset.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>The source code is publicly available at https:\/\/github.com\/1075793472\/AFTGAN.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad052","type":"journal-article","created":{"date-parts":[[2023,1,24]],"date-time":"2023-01-24T13:52:45Z","timestamp":1674568365000},"source":"Crossref","is-referenced-by-count":32,"title":["AFTGAN: prediction of multi-type PPI based on attention free transformer and graph attention network"],"prefix":"10.1093","volume":"39","author":[{"given":"Yanlei","family":"Kang","sequence":"first","affiliation":[{"name":"Zhejiang Province Key Laboratory of Smart Management & Application of Modern Agricultural Resources, School of Information Engineering, Huzhou University , Huzhou, Zhejiang 313000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7115-9751","authenticated-orcid":false,"given":"Arne","family":"Elofsson","sequence":"additional","affiliation":[{"name":"Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University , Stockholm, Solna 17121, Sweden"}]},{"given":"Yunliang","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Zhejiang Normal University , Jinhua, Zhejiang 321004, China"}]},{"given":"Weihong","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Science, Zhejiang Sci-Tech University , Hangzhou, Zhejiang 310018, China"}]},{"given":"Minzhe","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Science, Zhejiang Sci-Tech University , Hangzhou, Zhejiang 310018, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1767-1519","authenticated-orcid":false,"given":"Zhong","family":"Li","sequence":"additional","affiliation":[{"name":"Zhejiang Province Key Laboratory of Smart Management & Application of Modern Agricultural Resources, School of Information Engineering, Huzhou University , Huzhou, Zhejiang 313000, China"},{"name":"Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University , Stockholm, Solna 17121, Sweden"},{"name":"College of Science, Zhejiang Sci-Tech University , Hangzhou, Zhejiang 310018, China"}]}],"member":"286","published-online":{"date-parts":[[2023,1,24]]},"reference":[{"key":"2023041120300142200_","author":"Agarap","year":"2018"},{"key":"2023041120300142200_","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1042\/bj1280737","article-title":"The formation and stabilization of protein structure","volume":"128","author":"Anfinsen","year":"1972","journal-title":"Biochem. 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