title: Constructing lncRNA functional similarity network based on lncRNA-disease associations and disease semantic similarity.
abstract: Increasing evidence has indicated that plenty of lncRNAs play important roles in many critical biological processes. Developing powerful computational models to construct lncRNA functional similarity network based on heterogeneous biological datasets is one of the most important and popular topics in the fields of both lncRNAs and complex diseases. Functional similarity network construction could benefit the model development for both lncRNA function inference and lncRNA-disease association identification. However, little effort has been attempted to analysis and calculate lncRNA functional similarity on a large scale. In this study, based on the assumption that functionally similar lncRNAs tend to be associated with similar diseases, we developed two novel lncRNA functional similarity calculation models (LNCSIM). LNCSIM was evaluated by introducing similarity scores into the model of Laplacian Regularized Least Squares for LncRNA-Disease Association (LRLSLDA) for lncRNA-disease association prediction. As a result, new predictive models improved the performance of LRLSLDA in the leave-one-out cross validation of various known lncRNA-disease associations datasets. Furthermore, some of the predictive results for colorectal cancer and lung cancer were verified by independent biological experimental studies. It is anticipated that LNCSIM could be a useful and important biological tool for human disease diagnosis, treatment, and prevention.
authors.affiliation: 1] National Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences, Beijing, 100190, China [2] Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China.
authors.forename: Xing
authors.initials: X
authors.lastname: Chen
authors.affiliation: Department of Automation, Tsinghua University, Beijing, 100084, China.
authors.forename: Chenggang Clarence
authors.initials: CC
authors.lastname: Yan
authors.affiliation: Department of Automation, Tsinghua University, Beijing, 100084, China.
authors.forename: Cai
authors.initials: C
authors.lastname: Luo
authors.affiliation: Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China.
authors.forename: Wen
authors.initials: W
authors.lastname: Ji
authors.affiliation: Key Lab of Intelligent Information Processing of Chinese Academy of Sciences, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China.
authors.forename: Yongdong
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Department of Automation, Tsinghua University, Beijing, 100084, China.
authors.forename: Qionghai
authors.initials: Q
authors.lastname: Dai
mesh_terms: D015179:Colorectal Neoplasms / Q000235:genetics*; D019295:Computational Biology; D003198:Computer Simulation; D005260:Female; D015723:Gene Library; D053263:Gene Regulatory Networks / Q000235:genetics*; D020022:Genetic Predisposition to Disease; D006801:Humans; D008175:Lung Neoplasms / Q000235:genetics*; D008297:Male; D008954:Models, Biological; D062085:RNA, Long Noncoding / Q000235:genetics*
chemical_list: D062085:RNA, Long Noncoding