{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:30:13Z","timestamp":1772119813128,"version":"3.50.1"},"reference-count":24,"publisher":"Oxford University Press (OUP)","issue":"15","license":[{"start":{"date-parts":[[2021,2,3]],"date-time":"2021-02-03T00:00:00Z","timestamp":1612310400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100000001","name":"US National Science Foundation","doi-asserted-by":"crossref","award":["IIS-2008208"],"award-info":[{"award-number":["IIS-2008208"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000001","name":"US National Science Foundation","doi-asserted-by":"crossref","award":["1934600"],"award-info":[{"award-number":["1934600"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000001","name":"US National Science Foundation","doi-asserted-by":"crossref","award":["1938167"],"award-info":[{"award-number":["1938167"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000001","name":"US National Science Foundation","doi-asserted-by":"crossref","award":["1955151"],"award-info":[{"award-number":["1955151"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,8,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Many real-world biomedical interactions such as \u2018gene-disease\u2019, \u2018disease-symptom\u2019 and \u2018drug-target\u2019 are modeled as a bipartite network structure. Learning meaningful representations for such networks is a fundamental problem in the research area of Network Representation Learning (NRL). NRL approaches aim to translate the network structure into low-dimensional vector representations that are useful to a variety of biomedical applications. Despite significant advances, the existing approaches still have certain limitations. First, a majority of these approaches do not model the unique topological properties of bipartite networks. Consequently, their straightforward application to the bipartite graphs yields unsatisfactory results. Second, the existing approaches typically learn representations from static networks. This is limiting for the biomedical bipartite networks that evolve at a rapid pace, and thus necessitate the development of approaches that can update the representations in an online fashion.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this research, we propose a novel representation learning approach that accurately preserves the intricate bipartite structure, and efficiently updates the node representations. Specifically, we design a customized autoencoder that captures the proximity relationship between nodes participating in the bipartite bicliques (2\u2009\u00d7\u20092 sub-graph), while preserving both the global and local structures. Moreover, the proposed structure-preserving technique is carefully interleaved with the central tenets of continual machine learning to design an incremental learning strategy that updates the node representations in an online manner. Taken together, the proposed approach produces meaningful representations with high fidelity and computational efficiency. Extensive experiments conducted on several biomedical bipartite networks validate the effectiveness and rationality of the proposed approach.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab067","type":"journal-article","created":{"date-parts":[[2021,1,28]],"date-time":"2021-01-28T18:40:40Z","timestamp":1611859240000},"page":"2190-2197","source":"Crossref","is-referenced-by-count":6,"title":["Continual representation learning for evolving biomedical bipartite networks"],"prefix":"10.1093","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0826-445X","authenticated-orcid":false,"given":"Kishlay","family":"Jha","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Virginia , Charlottesville, VA 22904, USA"}]},{"given":"Guangxu","family":"Xun","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Virginia , Charlottesville, VA 22904, USA"}]},{"given":"Aidong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Virginia , Charlottesville, VA 22904, USA"}]}],"member":"286","published-online":{"date-parts":[[2021,2,3]]},"reference":[{"key":"2023061310444271900_btab067-B1","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1016\/j.ygeno.2009.08.016","article-title":"Ontological discovery environment: a system for integrating gene\u2013phenotype associations","volume":"94","author":"Baker","year":"2009","journal-title":"Genomics"},{"key":"2023061310444271900_btab067-B2","first-page":"585","author":"Belkin","year":"2002"},{"key":"2023061310444271900_btab067-B3","first-page":"1","article-title":"Lifelong machine learning","volume":"10","author":"Chen","year":"2016","journal-title":"Synth. 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