{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T05:48:25Z","timestamp":1782798505384,"version":"3.54.5"},"reference-count":44,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2022,1,18]],"date-time":"2022-01-18T00:00:00Z","timestamp":1642464000000},"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\/501100004761","name":"Natural Science Foundation of Hunan Province","doi-asserted-by":"crossref","award":["2021JJ30139"],"award-info":[{"award-number":["2021JJ30139"]}],"id":[{"id":"10.13039\/501100004761","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Changsha Key Research and Development Program","award":["KQ2004011"],"award-info":[{"award-number":["KQ2004011"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61773157"],"award-info":[{"award-number":["61773157"]}],"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><jats:p>Drug repositioning is an efficient and promising strategy for traditional drug discovery and development. Many research efforts are focused on utilizing deep-learning approaches based on a heterogeneous network for modeling complex drug\u2013disease associations. Similar to traditional latent factor models, which directly factorize drug\u2013disease associations, they assume the neighbors are independent of each other in the network and thus tend to be ineffective to capture localized information. In this study, we propose a novel neighborhood and neighborhood interaction-based neural collaborative filtering approach (called DRWBNCF) to infer novel potential drugs for diseases. Specifically, we first construct three networks, including the known drug\u2013disease association network, the drug\u2013drug similarity and disease\u2013disease similarity networks (using the nearest neighbors). To take the advantage of localized information in the three networks, we then design an integration component by proposing a new weighted bilinear graph convolution operation to integrate the information of the known drug\u2013disease association, the drug\u2019s and disease\u2019s neighborhood and neighborhood interactions into a unified representation. Lastly, we introduce a prediction component, which utilizes the multi-layer perceptron optimized by the \u03b1-balanced focal loss function and graph regularization to model the complex drug\u2013disease associations. Benchmarking comparisons on three datasets verified the effectiveness of DRWBNCF for drug repositioning. Importantly, the unknown drug\u2013disease associations predicted by DRWBNCF were validated against clinical trials and three authoritative databases and we listed several new DRWBNCF-predicted potential drugs for breast cancer (e.g. valrubicin and teniposide) and small cell lung cancer (e.g. valrubicin and cytarabine).<\/jats:p>","DOI":"10.1093\/bib\/bbab581","type":"journal-article","created":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T15:31:22Z","timestamp":1642001482000},"source":"Crossref","is-referenced-by-count":177,"title":["A weighted bilinear neural collaborative filtering approach for drug repositioning"],"prefix":"10.1093","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2384-1158","authenticated-orcid":false,"given":"Yajie","family":"Meng","sequence":"first","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, 410082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9263-8463","authenticated-orcid":false,"given":"Changcheng","family":"Lu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, 410082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Jin","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, 410082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junlin","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, 410082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangxiang","family":"Zeng","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, 410082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jialiang","family":"Yang","sequence":"additional","affiliation":[{"name":"Geneis Beijing Co., Ltd, Beijing, 100102, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,1,18]]},"reference":[{"key":"2022031506303231700_ref1","doi-asserted-by":"crossref","first-page":"1878","DOI":"10.1093\/bib\/bby061","article-title":"Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases","volume":"20","author":"Rifaioglu","year":"2019","journal-title":"Brief Bioinform"},{"key":"2022031506303231700_ref2","first-page":"793","article-title":"Alterations in processes and priorities needed for new drug development","volume":"145","author":"Fisher","year":"2006","journal-title":"Am Coll 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