title: IRWRLDA: improved random walk with restart for lncRNA-disease association prediction.
abstract: In recent years, accumulating evidences have shown that the dysregulations of lncRNAs are associated with a wide range of human diseases. It is necessary and feasible to analyze known lncRNA-disease associations, predict potential lncRNA-disease associations, and provide the most possible lncRNA-disease pairs for experimental validation. Considering the limitations of traditional Random Walk with Restart (RWR), the model of Improved Random Walk with Restart for LncRNA-Disease Association prediction (IRWRLDA) was developed to predict novel lncRNA-disease associations by integrating known lncRNA-disease associations, disease semantic similarity, and various lncRNA similarity measures. The novelty of IRWRLDA lies in the incorporation of lncRNA expression similarity and disease semantic similarity to set the initial probability vector of the RWR. Therefore, IRWRLDA could be applied to diseases without any known related lncRNAs. IRWRLDA significantly improved previous classical models with reliable AUCs of 0.7242 and 0.7872 in two known lncRNA-disease association datasets downloaded from the lncRNADisease database, respectively. Further case studies of colon cancer and leukemia were implemented for IRWRLDA and 60% of lncRNAs in the top 10 prediction lists have been confirmed by recent experimental reports.
authors: School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, 221116, China.
authors: Xing
authors: X
authors: Chen
authors: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China.
authors: Zhu-Hong
authors: ZH
authors: You
authors: Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China.|National Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences, Beijing, 100190, China.
authors: Gui-Ying
authors: GY
authors: Yan
authors: School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, 221116, China.
authors: Dun-Wei
authors: DW
authors: Gong
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D003110:Colonic Neoplasms / Q000235:genetics* / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D030541:Databases, Genetic; D020022:Genetic Predisposition to Disease; D006801:Humans; D007938:Leukemia / Q000235:genetics* / Q000378:metabolism; D015233:Models, Statistical; D016011:Normal Distribution; D011336:Probability; D062085:RNA, Long Noncoding / Q000235:genetics*; D015203:Reproducibility of Results
chemical_list: D062085:RNA, Long Noncoding
keywords: cancer; disease; lncRNAs; random walk with restart