{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T16:18:50Z","timestamp":1774023530023,"version":"3.50.1"},"reference-count":46,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2021,4,12]],"date-time":"2021-04-12T00:00:00Z","timestamp":1618185600000},"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":["61972135"],"award-info":[{"award-number":["61972135"]}],"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":["62031003"],"award-info":[{"award-number":["62031003"]}],"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":["LH2019F049"],"award-info":[{"award-number":["LH2019F049"]}],"id":[{"id":"10.13039\/501100005046","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":["2019M650069"],"award-info":[{"award-number":["2019M650069"]}],"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":["2020M670939"],"award-info":[{"award-number":["2020M670939"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hei-longjiang Postdoctoral Scientific Research Staring Foundation","award":["BHLQ18104"],"award-info":[{"award-number":["BHLQ18104"]}]},{"name":"Fundamental Research Foundation of Universi-ties in Heilongjiang Province for Technology Innovation","award":["KJCX201805"],"award-info":[{"award-number":["KJCX201805"]}]},{"name":"Innovation Talents Project of Harbin Science and Technology Bureau","award":["2017RAQXJ094"],"award-info":[{"award-number":["2017RAQXJ094"]}]},{"name":"Fundamental Research Foundation of Universities in Heilongjiang Province for Youth Innovation Team","award":["RCYJTD201805"],"award-info":[{"award-number":["RCYJTD201805"]}]},{"name":"Foundation of Graduate Innovative Research","award":["YJSCX2020-073HLJU"],"award-info":[{"award-number":["YJSCX2020-073HLJU"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,9,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Identifying the proteins that interact with drugs can reduce the cost and time of drug development. Existing computerized methods focus on integrating drug-related and protein-related data from multiple sources to predict candidate drug\u2013target interactions (DTIs). However, multi-scale neighboring node sequences and various kinds of drug and protein similarities are neither fully explored nor considered in decision making.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We propose a drug-target interaction prediction method, DTIP, to encode and integrate multi-scale neighbouring topologies, multiple kinds of similarities, associations, interactions related to drugs and proteins. We firstly construct a three-layer heterogeneous network to represent interactions and associations across drug, protein, and disease nodes. Then a learning framework based on fully-connected autoencoder is proposed to learn the nodes\u2019 low-dimensional feature representations within the heterogeneous network. Secondly, multi-scale neighbouring sequences of drug and protein nodes are formulated by random walks. A module based on bidirectional gated recurrent unit is designed to learn the neighbouring sequential information and integrate the low-dimensional features of nodes. Finally, we propose attention mechanisms at feature level, neighbouring topological level and similarity level to learn more informative features, topologies and similarities. The prediction results are obtained by integrating neighbouring topologies, similarities and feature attributes using a multiple layer CNN. Comprehensive experimental results over public dataset demonstrated the effectiveness of our innovative features and modules. Comparison with other state-of-the-art methods and case studies of five drugs further validated DTIP\u2019s ability in discovering the potential candidate drug-related proteins.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bib\/bbab119","type":"journal-article","created":{"date-parts":[[2021,3,15]],"date-time":"2021-03-15T12:09:42Z","timestamp":1615810182000},"source":"Crossref","is-referenced-by-count":19,"title":["Integrating multi-scale neighbouring topologies and cross-modal similarities for drug\u2013protein interaction prediction"],"prefix":"10.1093","volume":"22","author":[{"given":"Ping","family":"Xuan","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China"}]},{"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China"}]},{"given":"Hui","family":"Cui","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Technology, La Trobe University, Melbourne 3083, Australia"}]},{"given":"Tiangang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mathematical Science, Heilongjiang University, Harbin 150080, China"}]},{"given":"Maozu","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, 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":[[2021,4,12]]},"reference":[{"issue":"4","key":"2021090815432038500_ref1","doi-asserted-by":"crossref","first-page":"696","DOI":"10.1093\/bib\/bbv066","article-title":"Drug-target interaction 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