ILNCSIM: improved lncRNA functional similarity calculation model

// Yu-An Huang 1, * , Xing Chen 2, 3, * , Zhu-Hong You 4 , De-Shuang Huang 5 , Keith C.C. Chan 6 1 College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China 2 Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China 3 National Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences, Beijing, 100190, China 4 School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China 5 School of Electronics and Information Engineering, Tongji University, Shanghai, 201804, China 6 Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China * The first two authors should be regarded as joint first authors Correspondence to: Xing Chen, email: xingchen@amss.ac.cn Zhu-Hong You, email: zhuhongyou@gmail.com Keywords: lncRNAs, functional similarity, disease, cancer, directed acyclic graph Received: January 4, 2016     Accepted: March 4, 2016     Published: March 23, 2016 ABSTRACT Increasing observations have indicated that lncRNAs play a significant role in various critical biological processes and the development and progression of various human diseases. Constructing lncRNA functional similarity networks could benefit the development of computational models for inferring lncRNA functions and identifying lncRNA-disease associations. However, little effort has been devoted to quantifying lncRNA functional similarity. In this study, we developed an Improved LNCRNA functional SIMilarity calculation model (ILNCSIM) based on the assumption that lncRNAs with similar biological functions tend to be involved in similar diseases. The main improvement comes from the combination of the concept of information content and the hierarchical structure of disease directed acyclic graphs for disease similarity calculation. ILNCSIM was combined with the previously proposed model of Laplacian Regularized Least Squares for lncRNA-Disease Association to further evaluate its performance. As a result, new model obtained reliable performance in the leave-one-out cross validation (AUCs of 0.9316 and 0.9074 based on MNDR and Lnc2cancer databases, respectively), and 5-fold cross validation (AUCs of 0.9221 and 0.9033 for MNDR and Lnc2cancer databases), which significantly improved the prediction performance of previous models. It is anticipated that ILNCSIM could serve as an effective lncRNA function prediction model for future biomedical researches.