{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T01:33:14Z","timestamp":1770341594384,"version":"3.49.0"},"reference-count":49,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2022,4,8]],"date-time":"2022-04-08T00:00:00Z","timestamp":1649376000000},"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\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62172143"],"award-info":[{"award-number":["62172143"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972135"],"award-info":[{"award-number":["61972135"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005046","name":"Natural Science Foundation of Heilongjiang Province","doi-asserted-by":"publisher","award":["LH2019A029"],"award-info":[{"award-number":["LH2019A029"]}],"id":[{"id":"10.13039\/501100005046","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2020M670939"],"award-info":[{"award-number":["2020M670939"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2019M650069"],"award-info":[{"award-number":["2019M650069"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Heilongjiang Postdoctoral Scientific Research Staring Foundation","award":["BHLQ18104"],"award-info":[{"award-number":["BHLQ18104"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,5,13]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>Identifying new uses of approved drugs is an effective way to reduce the time and cost of drug development. Recent computational approaches for predicting drug\u2013disease associations have integrated multi-sourced data on drugs and diseases. However, neighboring topologies of various scales in multiple heterogeneous drug\u2013disease networks have yet to be exploited and fully integrated.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We propose a novel method for drug\u2013disease association prediction, called MGPred, used to encode and learn multi-scale neighboring topologies of drug and disease nodes and pairwise attributes from heterogeneous networks. First, we constructed three heterogeneous networks based on multiple kinds of drug similarities. Each network comprises drug and disease nodes and edges created based on node-wise similarities and associations that reflect specific topological structures. We also propose an embedding mechanism to formulate topologies that cover different ranges of neighbors. To encode the embeddings and derive multi-scale neighboring topology representations of drug and disease nodes, we propose a module based on graph convolutional autoencoders with shared parameters for each heterogeneous network. We also propose scale-level attention to obtain an adaptive fusion of informative topological representations at different scales. Finally, a learning module based on a convolutional neural network with various receptive fields is proposed to learn multi-view attribute representations of a pair of drug and disease nodes. Comprehensive experiment results demonstrate that MGPred outperforms other state-of-the-art methods in comparison to drug-related disease prediction, and the recall rates for the top-ranked candidates and case studies on five drugs further demonstrate the ability of MGPred to retrieve potential drug\u2013disease associations.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bib\/bbac123","type":"journal-article","created":{"date-parts":[[2022,3,15]],"date-time":"2022-03-15T12:21:13Z","timestamp":1647346873000},"source":"Crossref","is-referenced-by-count":3,"title":["Heterogeneous multi-scale neighbor topologies enhanced drug\u2013disease association prediction"],"prefix":"10.1093","volume":"23","author":[{"given":"Ping","family":"Xuan","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China"},{"name":"School of Computer Science, Shaanxi Normal University, Xi\u2019an 710062, China"}]},{"given":"Xiangfeng","family":"Meng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China"}]},{"given":"Ling","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China"}]},{"given":"Tiangang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China"}]},{"given":"Toshiya","family":"Nakaguchi","sequence":"additional","affiliation":[{"name":"Center for Frontier Medical Engineering, Chiba University, Chiba 2638522, Japan"}]}],"member":"286","published-online":{"date-parts":[[2022,4,8]]},"reference":[{"issue":"1","key":"2022051813445276800_ref1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.drudis.2018.06.012","article-title":"Renovation as innovation: is repurposing the future of drug 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