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Aiming at the problem of unstable prediction accuracy in the current method of building negative sets using random sampling, we proposed a method of constructing negative sets based on a conditional generative adversarial network (CGAN), named PPIGAN. This method generates negative samples through a generative network, and the PPI prediction model uses these generated negative samples along with positive samples to learn interaction features. Simultaneously, the generator and the prediction model continuously compete against each other during the learning process, which enhances the model\u2019s generalization ability and prediction accuracy. Experimental results show that the accuracy of our proposed method reaches 94.68% and 98.22% in 5-fold cross-validation on yeast and human datasets, respectively. These results either surpass or closely approach the performance of advanced PPI prediction models such as PIPR, convolutional neural network, DeepTrio, and DeepFE, indicating that the method proposed in this article provides an effective solution for the work related to PPI prediction.<\/jats:p>","DOI":"10.1177\/15578666261453608","type":"journal-article","created":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T10:32:29Z","timestamp":1781001149000},"page":"804-816","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["PPIGAN: Prediction of Protein\u2013Protein Interactions Using Generative Adversarial Networks"],"prefix":"10.1177","volume":"33","author":[{"given":"Xu","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Information Engineering, Northwest A&amp;F University, Yangling, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Songyan","family":"Xue","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Northwest A&amp;F University, Yangling, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Geng","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Northwest A&amp;F University, Yangling, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiehuizhi","family":"Wen","sequence":"additional","affiliation":[{"name":"College of Forestry, Northwest A&amp;F University, Yangling, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lingwei","family":"Lai","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Northwest A&amp;F University, Yangling, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lvwen","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Northwest A&amp;F University, Yangling, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7134-9021","authenticated-orcid":false,"given":"Jiantao","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Northwest A&amp;F University, Yangling, China."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,6,9]]},"reference":[{"key":"e_1_3_3_2_1","unstructured":"Arjovsky M Chintala S Bottou L. and Wasserstein generative adversarial networks. 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