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Computational tools, particularly those based on deep learning, are preferred for the efficient prediction of PPIs. Despite recent progress, two challenges remain unresolved: (i) the imbalanced nature of PPI characteristics is often ignored and (ii) there exists a high computational cost associated with capturing long-range dependencies within protein data, typically exhibiting quadratic complexity relative to the length of the protein sequence.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Result<\/jats:title>\n                  <jats:p>Here, we propose an anti-symmetric graph learning model, BaPPI, for the balanced prediction of PPIs and extrapolation of the involved patterns in PPI network. In BaPPI, the contextualized information of protein data is efficiently handled by an attention-free mechanism formed by recurrent convolution operator. The anti-symmetric graph convolutional network is employed to model the uneven distribution within PPI networks, aiming to learn a more robust and balanced representation of the relationships between proteins. Ultimately, the model is updated using asymmetric loss. The experimental results on classical baseline datasets demonstrate that BaPPI outperforms four state-of-the-art PPI prediction methods. In terms of Micro-F1, BaPPI exceeds the second-best method by 6.5% on SHS27K and 5.3% on SHS148K. Further analysis of the generalization ability and patterns of predicted PPIs also demonstrates our model\u2019s generalizability and robustness to the imbalanced nature of PPI datasets.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The source code of this work is publicly available at https:\/\/github.com\/ttan6729\/BaPPI.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae603","type":"journal-article","created":{"date-parts":[[2024,10,15]],"date-time":"2024-10-15T16:39:35Z","timestamp":1729010375000},"source":"Crossref","is-referenced-by-count":15,"title":["Anti-symmetric framework for balanced learning of protein\u2013protein interactions"],"prefix":"10.1093","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1207-4192","authenticated-orcid":false,"given":"Tao","family":"Tang","sequence":"first","affiliation":[{"name":"School of Modern Posts, Nanjing University of Posts and Telecommunications , Nanjing 210023,","place":["China"]}]},{"given":"Tianyang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Modern Posts, Nanjing University of Posts and Telecommunications , Nanjing 210023,","place":["China"]}]},{"given":"Weizhuo","family":"Li","sequence":"additional","affiliation":[{"name":"School of Modern Posts, Nanjing University of Posts and Telecommunications , Nanjing 210023,","place":["China"]}]},{"given":"Xiaofeng","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Jilin University , Changchun 130012,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7680-3155","authenticated-orcid":false,"given":"Yuansheng","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University , Changsha 410086,","place":["China"]}]},{"given":"Xiangxiang","family":"Zeng","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University , Changsha 410086,","place":["China"]}]}],"member":"286","published-online":{"date-parts":[[2024,10,15]]},"reference":[{"key":"2024102718413121600_btae603-B1","doi-asserted-by":"crossref","first-page":"2312","DOI":"10.1038\/s41467-018-04632-8","article-title":"Network biology discovers pathogen contact points in host protein-protein interactomes","volume":"9","author":"Ahmed","year":"2018","journal-title":"Nat 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