FMLNCSIM: fuzzy measure-based lncRNA functional similarity calculation model
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Xing Chen1,*, Yu-An Huang2,*, Xue-Song Wang1, Zhu-Hong You3, Keith C.C. Chan2
1School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, China
2Department of Computing, Hong Kong Polytechnic University, Hong Kong
3School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China
*The first two authors should be regarded as joint First Authors
Xing Chen, email: email@example.com
Zhu-Hong You, email: firstname.lastname@example.org
Keywords: lncRNAs, functional similarity, disease, fuzzy measure, directed acyclic graph
Received: April 05, 2016 Accepted: May 29, 2016 Published: June 14, 2016
Accumulating experimental studies have indicated the influence of lncRNAs on various critical biological processes as well as disease development and progression. Calculating lncRNA functional similarity is of high value in inferring lncRNA functions and identifying potential lncRNA-disease associations. However, little effort has been attempt to measure the functional similarity among lncRNAs on a large scale. In this study, we developed a Fuzzy Measure-based LNCRNA functional SIMilarity calculation model (FMLNCSIM) based on the assumption that functionally similar lncRNAs tend to be associated with similar diseases. The performance improvement of FMLNCSIM mainly comes from the combination of information content and the concept of fuzzy measure, which was applied to the directed acyclic graphs of disease MeSH descriptors. To evaluate the effectiveness of FMLNCSIM, we further combined it with the previously proposed model of Laplacian Regularized Least Squares for lncRNA-Disease Association (LRLSLDA). As a result, the integrated model, LRLSLDA-FMLNCSIM, achieve good performance in the frameworks of global LOOCV (AUCs of 0.8266 and 0.9338 based on LncRNADisease and MNDR database) and 5-fold cross validation (average AUCs of 0.7979 and 0.9237 based on LncRNADisease and MNDR database), which significantly improve the performance of previous classical models. It is anticipated that FMLNCSIM could be used for searching functionally similar lncRNAs and inferring lncRNA functions in the future researches.
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