{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T04:14:54Z","timestamp":1768882494536,"version":"3.49.0"},"reference-count":54,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2022,1,17]],"date-time":"2022-01-17T00:00:00Z","timestamp":1642377600000},"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":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62172355"],"award-info":[{"award-number":["62172355"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61702444"],"award-info":[{"award-number":["61702444"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,3,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Predicting drug\u2013target interactions (DTIs) is a convenient strategy for drug discovery. Although various computational methods have been put forward in recent years, DTIs prediction is still a challenging task. In this paper, based on indirect prior information (we term them as mediators), we proposed a new model, called Bridging-BPs (bridging paths), for DTIs prediction. Specifically, we regarded linkage process between mediators and DTs (drugs and proteins) as \u2018bridging\u2019 and source (drug)-mediators-destination (protein) as bridging paths. By integrating various bridging paths, we constructed a bridging heterogeneous graph for DTIs. After that, an improved graph-embedding algorithm\u2014BPs2vec\u2014was designed to capture deep topological features underlying the bridging graph, thereby obtaining the low-dimensional node vector representations. Then, the vector representations were fed into a Random Forest classifier to train and score the probability, outputting the final classification results for potential DTIs. Under 5-fold cross validation, our method obtained AUPR of 88.97% and AUC of 88.63%, suggesting that Bridging-BPs could effectively mine the link relationships hidden in indirect prior information and it significantly improved the accuracy and robustness of DTIs prediction without direct prior information. Finally, we confirmed the practical prediction ability of Bridging-BPs by case studies.<\/jats:p>","DOI":"10.1093\/bib\/bbab557","type":"journal-article","created":{"date-parts":[[2021,12,6]],"date-time":"2021-12-06T12:09:44Z","timestamp":1638792584000},"source":"Crossref","is-referenced-by-count":8,"title":["Bridging-BPs: a novel approach to predict potential drug\u2013target interactions based on a bridging heterogeneous graph and BPs2vec"],"prefix":"10.1093","volume":"23","author":[{"given":"Guodong","family":"Li","sequence":"first","affiliation":[{"name":"College of Life Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6831-1807","authenticated-orcid":false,"given":"Ping","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Informatics, Huazhong Agricultural University, Wuhan, 430070, 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