Prediction of lncRNA-disease associations by integrating diverse heterogeneous information sources with RWR algorithm and positive pointwise mutual information

作     者：Fan, Xiao-Nan Zhang, Shao-Wu Zhang, Song-Yao Zhu, Kunju Lu, Songjian 

作者机构：Northwestern Polytech Univ Key Lab Informat Fus Technol Minist Educ Sch Automat 127 West Youyi Rd Xian 710072 Shaanxi Peoples R China Univ Pittsburgh Dept Biomed Informat 5607 Baum Blvd Pittsburgh PA 15206 USA Jinan Univ Affiliated Hosp 1 Guangzhou Guangdong Peoples R China Jinan Univ Clin Med Res Inst Guangzhou Guangdong Peoples R China 

出 版 物：《BMC BIOINFORMATICS》 (英国医学委员会:生物信息)

年 卷 期：2019年第20卷第1期

页      面：1-12页

核心收录：

学科分类：0710[理学-生物学] 0836[工学-生物工程] 10[医学] 

基　　金：National Natural Science Foundation of China [61873202, 61473232, 91430111] National Library of Medicine grants of United States [R00LM011673] 

主　　题：Long noncoding RNA Disease lncRNA-disease association Heterogeneous network Random walk with restart algorithm 

摘      要：BackgroundLong non-coding RNAs play an important role in human complex diseases. Identification of lncRNA-disease associations will gain insight into disease-related lncRNAs and benefit disease diagnoses and treatment. However, using experiments to explore the lncRNA-disease associations is expensive and time *** this study, we developed a novel method to identify potential lncRNA-disease associations by Integrating Diverse Heterogeneous Information sources with positive pointwise Mutual Information and Random Walk with restart algorithm (namely IDHI-MIRW). IDHI-MIRW first constructs multiple lncRNA similarity networks and disease similarity networks from diverse lncRNA-related and disease-related datasets, then implements the random walk with restart algorithm on these similarity networks for extracting the topological similarities which are fused with positive pointwise mutual information to build a large-scale lncRNA-disease heterogeneous network. Finally, IDHI-MIRW implemented random walk with restart algorithm on the lncRNA-disease heterogeneous network to infer potential lncRNA-disease *** with other state-of-the-art methods, IDHI-MIRW achieves the best prediction performance. In case studies of breast cancer, stomach cancer, and colorectal cancer, 36/45 (80%) novel lncRNA-disease associations predicted by IDHI-MIRW are supported by recent literatures. Furthermore, we found lncRNA LINC01816 is associated with the survival of colorectal cancer patients. IDHI-MIRW is freely available at https://***/NWPU-903PR/IDHI-MIRW.