{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T13:13:12Z","timestamp":1770815592823,"version":"3.50.1"},"reference-count":36,"publisher":"Oxford University Press (OUP)","issue":"14","license":[{"start":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T00:00:00Z","timestamp":1653955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62102269"],"award-info":[{"award-number":["62102269"]}],"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":["62131004"],"award-info":[{"award-number":["62131004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2021M690029"],"award-info":[{"award-number":["2021M690029"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Foundation Project of Shenzhen Polytechnic","award":["6022310029K"],"award-info":[{"award-number":["6022310029K"]}]},{"name":"Special Science Foundation of Quzhou","award":["2021D004"],"award-info":[{"award-number":["2021D004"]}]},{"name":"Natural Science Foundation of Jiangsu Higher Education Institutions of China","award":["20KJB180012"],"award-info":[{"award-number":["20KJB180012"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>Phytopathogenic fungi secrete effector proteins to subvert host defenses and facilitate infection. Systematic analysis and prediction of candidate fungal effector proteins are crucial for experimental validation and biological control of plant disease. However, two problems are still considered intractable to be solved in fungal effector prediction: one is the high-level diversity in effector sequences that increases the difficulty of protein feature learning, and the other is the class imbalance between effector and non-effector samples in the training dataset.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In our study, pretrained deep representation learning methods are presented to represent multiple characteristics of sequences for predicting fungal effectors and generative adversarial networks are adapted to create synthetic feature samples to address the data imbalance problem. Compared with the state-of-the-art fungal effector prediction methods, Effector-GAN shows an overall improvement in accuracy in the independent test set.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>Effector-GAN offers a user-friendly interface to inspect potential fungal effector proteins (http:\/\/lab.malab.cn\/~wys\/webserver\/Effector-GAN). The Python script can be downloaded from http:\/\/lab.malab.cn\/~wys\/gitlab\/effector-gan.<\/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\/btac374","type":"journal-article","created":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:43:31Z","timestamp":1654044211000},"page":"3541-3548","source":"Crossref","is-referenced-by-count":18,"title":["Effector-GAN: prediction of fungal effector proteins based on pretrained deep representation learning methods and generative adversarial networks"],"prefix":"10.1093","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1274-4958","authenticated-orcid":false,"given":"Yansu","family":"Wang","sequence":"first","affiliation":[{"name":"Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China , Chengdu 610054, China"},{"name":"School of Electronic and Communication Engineering, Shenzhen Polytechnic , Shenzhen 518000, China"}]},{"given":"Ximei","family":"Luo","sequence":"additional","affiliation":[{"name":"Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China , Chengdu 610054, China"},{"name":"School of Electronic and Communication Engineering, Shenzhen Polytechnic , Shenzhen 518000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6406-1142","authenticated-orcid":false,"given":"Quan","family":"Zou","sequence":"additional","affiliation":[{"name":"Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China , Chengdu 610054, China"}]}],"member":"286","published-online":{"date-parts":[[2022,5,31]]},"reference":[{"key":"2023041405364739500_","doi-asserted-by":"crossref","first-page":"1315","DOI":"10.1038\/s41592-019-0598-1","article-title":"Unified rational protein engineering with sequence-based deep representation learning","volume":"16","author":"Alley","year":"2019","journal-title":"Nat. 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