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Therefore, developing powerful computational models to predict correlation between lncRNAs and diseases based on heterogeneous biological datasets will be important. However, there are few approaches to calculating and analyzing lncRNA-disease associations on the basis of information about miRNAs. In this article, a new computational method based on distance correlation set is developed to predict lncRNA-disease associations (DCSLDA). Comparing with existing state-of-the-art methods, we found that the major novelty of DCSLDA lies in the introduction of lncRNA-miRNA-disease network and distance correlation set; thus DCSLDA can be applied to predict potential lncRNA-disease associations without requiring any known disease-lncRNA associations. Simulation results show that DCSLDA can significantly improve previous existing models with reliable AUC of 0.8517 in the leave-one-out cross-validation. Furthermore, while implementing DCSLDA to prioritize candidate lncRNAs for three important cancers, in the first 0.5% of forecast results, 17 predicted associations are verified by other independent studies and biological experimental studies. Hence, it is anticipated that DCSLDA could be a great addition to the biomedical research field.<\/jats:p>","DOI":"10.1155\/2018\/6747453","type":"journal-article","created":{"date-parts":[[2018,6,26]],"date-time":"2018-06-26T19:32:36Z","timestamp":1530041556000},"page":"1-12","source":"Crossref","is-referenced-by-count":4,"title":["A Novel Approach for Predicting Disease-lncRNA Associations Based on the Distance Correlation Set and Information of the miRNAs"],"prefix":"10.1155","volume":"2018","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8794-3148","authenticated-orcid":true,"given":"Haochen","family":"Zhao","sequence":"first","affiliation":[{"name":"College of Information Engineering, Xiangtan University, Xiangtan 411105, China"},{"name":"Key Laboratory of Intelligent Computing & Information Processing, Xiangtan University, Xiangtan 411105, 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