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In recent years, various computational methods have been developed to uncover potential disease-gene associations. However, existing computational approaches for disease-gene association prediction still face two major limitations. First, most current studies focus on constructing complex heterogeneous graphs using multi-dimensional biological entity relationships, while overlooking critical latent interaction patterns, namely, disease neighbor interactions and gene neighbor interactions\u2014which are more valuable for association prediction. Second, in self-supervised learning (SSL), the presence of noise in auxiliary tasks commonly affects the accurate modeling of diseases and genes. In this study, we propose a novel denoising method for disease-gene association prediction, termed DGSL. To address the first issue, we utilize bipartite graphs corresponding to diseases and genes to derive disease-disease and gene-gene similarities, and further construct disease and gene interaction graphs to capture the latent interaction patterns. To tackle the second challenge, we implement cross-view denoising through adaptive semantic alignment in the embedding space, while preserving useful neighbor interactions. Extensive experiments on benchmark datasets demonstrate the effectiveness of our method.<\/jats:p>","DOI":"10.1186\/s12859-025-06281-3","type":"journal-article","created":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T12:02:58Z","timestamp":1761220978000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Denoising self-supervised learning for disease-gene association prediction"],"prefix":"10.1186","volume":"26","author":[{"given":"Yan","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ju","family":"Xiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianming","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,23]]},"reference":[{"issue":"10","key":"6281_CR1","doi-asserted-by":"publisher","first-page":"1589","DOI":"10.1007\/s12031-020-01600-0","volume":"70","author":"W Chen","year":"2020","unstructured":"Chen W, Wu L, Hu Y, Jiang L, Liang N, Chen J, et al. 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