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Computational methods are regarded as an effective way to associate drugs with new indications. However, most of them complete their tasks by constructing a variety of heterogeneous networks without considering the biological knowledge of drugs and diseases, which are believed to be useful for improving the accuracy of drug repositioning. To this end, a novel heterogeneous information network (HIN) based model, namely HINGRL, is proposed to precisely identify new indications for drugs based on graph representation learning techniques. More specifically, HINGRL first constructs a HIN by integrating drug\u2013disease, drug\u2013protein and protein\u2013disease biological networks with the biological knowledge of drugs and diseases. Then, different representation strategies are applied to learn the features of nodes in the HIN from the topological and biological perspectives. Finally, HINGRL adopts a Random Forest classifier to predict unknown drug\u2013disease associations based on the integrated features of drugs and diseases obtained in the previous step. Experimental results demonstrate that HINGRL achieves the best performance on two real datasets when compared with state-of-the-art models. Besides, our case studies indicate that the simultaneous consideration of network topology and biological knowledge of drugs and diseases allows HINGRL to precisely predict drug\u2013disease associations from a more comprehensive perspective. The promising performance of HINGRL also reveals that the utilization of rich heterogeneous information provides an alternative view for HINGRL to identify novel drug\u2013disease associations especially for new diseases.<\/jats:p>","DOI":"10.1093\/bib\/bbab515","type":"journal-article","created":{"date-parts":[[2021,11,16]],"date-time":"2021-11-16T12:09:18Z","timestamp":1637064558000},"source":"Crossref","is-referenced-by-count":139,"title":["HINGRL: predicting drug\u2013disease associations with graph representation learning on heterogeneous information networks"],"prefix":"10.1093","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8200-6016","authenticated-orcid":false,"given":"Bo-Wei","family":"Zhao","sequence":"first","affiliation":[{"name":"The Xinjiang Technical Institute of Physics and Chemistry , Chinese Academy of Sciences, Urumqi 830011, China"},{"name":"University of Chinese Academy of Sciences , Beijing 100049, China"},{"name":"Xinjiang Laboratory of 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