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It has received extensive attention regarding the tedious, time-consuming, and highly expensive procedure with a high risk of failure of new drug discovery. Data-driven approaches are an important class of methods that have been introduced for identifying a candidate drug against a target disease. In the present study, a model is proposed illustrating the integration of drug-disease association data for drug repurposing using a deep neural network. The model, so-called IDDI-DNN, primarily constructs similarity matrices for drug-related properties (three matrices), disease-related properties (two matrices), and drug-disease associations (one matrix). Then, these matrices are integrated into a unique matrix through a two-step procedure benefiting from the similarity network fusion method. The model uses a constructed matrix for the prediction of novel and unknown drug-disease associations through a convolutional neural network. The proposed model was evaluated comparatively using two different datasets including the gold standard dataset and DNdataset. Comparing the results of evaluations indicates that IDDI-DNN outperforms other state-of-the-art methods concerning prediction accuracy.<\/jats:p>","DOI":"10.1186\/s12859-023-05572-x","type":"journal-article","created":{"date-parts":[[2023,11,22]],"date-time":"2023-11-22T10:03:09Z","timestamp":1700647389000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["A novel efficient drug repurposing framework through drug-disease association data integration using convolutional neural networks"],"prefix":"10.1186","volume":"24","author":[{"given":"Ramin","family":"Amiri","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6320-8517","authenticated-orcid":false,"given":"Jafar","family":"Razmara","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sepideh","family":"Parvizpour","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Habib","family":"Izadkhah","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,22]]},"reference":[{"issue":"1","key":"5572_CR1","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.drudis.2017.08.008","volume":"23","author":"M Simsek","year":"2018","unstructured":"Simsek M, et al. 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