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EAGP: an efficient generative augmentation framework for phage protein classification under severe class imbalance.

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Title: EAGP: an efficient generative augmentation framework for phage protein classification under severe class imbalance.
Authors: Li J; Faculty of Information Science and Engineering, Ocean University of China, Qingdao, Shandong 266100, China., Li H; Faculty of Information Science and Engineering, Ocean University of China, Qingdao, Shandong 266100, China., Wang Y; Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, China.; Institute of Digital Health, Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324003, China., Zou Q; Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, China.; Institute of Digital Health, Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324003, China., Zhu H; Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China.
Source: Bioinformatics (Oxford, England) [Bioinformatics] 2026 Jun 01; Vol. 42 (6).
Publication Type: Journal Article
Journal Info: Publisher: Oxford University Press Country of Publication: England NLM ID: 9808944 Publication Model: Print Cited Medium: Internet ISSN: 1367-4811 (Electronic) Linking ISSN: 13674803 NLM ISO Abbreviation: Bioinformatics Subsets: MEDLINE
Database: MEDLINE Ultimate
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  Data: <searchLink fieldCode="AU" term="%22Li+J%22">Li J</searchLink>; Faculty of Information Science and Engineering, Ocean University of China, Qingdao, Shandong 266100, China.<br /><searchLink fieldCode="AU" term="%22Li+H%22">Li H</searchLink>; Faculty of Information Science and Engineering, Ocean University of China, Qingdao, Shandong 266100, China.<br /><searchLink fieldCode="AU" term="%22Wang+Y%22">Wang Y</searchLink>; Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, China.; Institute of Digital Health, Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324003, China.<br /><searchLink fieldCode="AU" term="%22Zou+Q%22">Zou Q</searchLink>; Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, China.; Institute of Digital Health, Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324003, China.<br /><searchLink fieldCode="AU" term="%22Zhu+H%22">Zhu H</searchLink>; Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China.
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  Data: <searchLink fieldCode="JN" term="%229808944%22">Bioinformatics (Oxford, England)</searchLink> [Bioinformatics] 2026 Jun 01; Vol. 42 (6).
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Oxford+University+Press%22">Oxford University Press </searchLink><i>Country of Publication: </i>England <i>NLM ID: </i>9808944 <i>Publication Model: </i>Print <i>Cited Medium: </i>Internet <i>ISSN: </i>1367-4811 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2213674803%22">13674803 </searchLink><i>NLM ISO Abbreviation: </i>Bioinformatics <i>Subsets: </i>MEDLINE
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        Value: 10.1093/bioinformatics/btag373
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              Text: 2026 Jun 01
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              Y: 2026
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