{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T06:50:08Z","timestamp":1762325408094,"version":"3.41.2"},"reference-count":31,"publisher":"World Scientific Pub Co Pte Ltd","issue":"05","funder":[{"name":"National Key Research and Development Program of China","award":["2018AAA0100100"],"award-info":[{"award-number":["2018AAA0100100"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Bioinform. Comput. Biol."],"published-print":{"date-parts":[[2022,10]]},"abstract":"<jats:p> Identification of potential drug\u2013target interactions (DTIs) plays a pivotal role in the development of drug and target discovery in the public healthcare sector. However, biological experiments for predicting interactions between drugs and targets are still expensive, complicated, and time-consuming. Thus, computational methods are widely applied for aiding drug\u2013target interaction prediction. In this paper, we propose a novel model, named GCMCDTI, for DTIs prediction which adopts a graph convolutional network based on matrix completion. We regard the association prediction between drugs and targets as link prediction and treat the process as matrix completion, and then a graph convolutional auto-encoder framework is employed to construct the drug and target embeddings. Then, a bilinear decoder is applied to reconstruct the DTI matrix. We conduct our experiments on four benchmark datasets consisting of enzymes, G protein-coupled receptors (GPCRs), ion channels, and nuclear receptors. The five-fold cross-validation results achieve the high average AUC values of 95.78%, 95.31%, 93.90%, and 91.77%, respectively. To further evaluate our method, we compare our proposed method with other state-of-the-art approaches. The comparison results illustrate that our proposed method obtains improvement in performance on DTI prediction. The proposed method will be a good choice in the field of DTI prediction. <\/jats:p>","DOI":"10.1142\/s0219720022500238","type":"journal-article","created":{"date-parts":[[2022,9,25]],"date-time":"2022-09-25T14:08:23Z","timestamp":1664114903000},"source":"Crossref","is-referenced-by-count":4,"title":["GCMCDTI: Graph convolutional autoencoder framework for predicting drug\u2013target interactions based on matrix completion"],"prefix":"10.1142","volume":"20","author":[{"given":"Jing","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, P.\u00a0R.\u00a0China"}]},{"given":"Chen","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, P.\u00a0R.\u00a0China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1644-1006","authenticated-orcid":false,"given":"Zhengwei","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, P.\u00a0R.\u00a0China"}]},{"given":"Ru","family":"Nie","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, P.\u00a0R.\u00a0China"}]},{"given":"Pengyong","family":"Han","sequence":"additional","affiliation":[{"name":"Central Lab Changzhi Medical College, Changzhi, Shanxi\u00a0046000, P.\u00a0R.\u00a0China"}]},{"given":"Wenjia","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, 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