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Traditional graph-theoretical approaches have been instrumental in mapping key biological processes using high-confidence interaction data. However, these methods often struggle with incomplete or\/and heterogeneous datasets. In this study, we extend beyond conventional bipartite models by integrating attribute-driven knowledge from the Molecular Signatures Database (MSigDB) using the node2vec algorithm.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Our approach explores unsupervised biological relationships and uncovers potential associations between genes and biological terms through network connectivity analysis. By embedding both human and mouse data into a shared vector space, we validate our findings cross-species, further strengthening the robustness of our method.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This integrative framework reveals both expected and novel biological insights, offering a comprehensive perspective that complements traditional biological network analysis and paves the way for deeper understanding of complex biological processes and diseases.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12859-025-06100-9","type":"journal-article","created":{"date-parts":[[2025,3,24]],"date-time":"2025-03-24T20:30:54Z","timestamp":1742848254000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Uncovering latent biological function associations through gene set embeddings"],"prefix":"10.1186","volume":"26","author":[{"given":"Yuhang","family":"Huang","sequence":"first","affiliation":[]},{"given":"Fan","family":"Zhong","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,24]]},"reference":[{"issue":"D1","key":"6100_CR1","doi-asserted-by":"crossref","first-page":"D353","DOI":"10.1093\/nar\/gkw1092","volume":"45","author":"M Kanehisa","year":"2017","unstructured":"Kanehisa M, et al. 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