BRWLDA: bi-random walks for predicting lncRNA-disease associations

// Guoxian Yu 1 , Guangyuan Fu 1 , Chang Lu 1 , Yazhou Ren 2 and Jun Wang 1 1 College of Computer and Information Sciences, Southwest University, Chongqing, China 2 Big Data Research Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China Correspondence to: Guoxian Yu, email: gxyu@swu.edu.cn Jun Wang, email: kingjun@swu.edu.cn Keywords: lncRNAs, diseases, lncRNA-disease associations, bi-relational network, bi-random walk Received: May 25, 2017     Accepted: June 19, 2017     Published: July 26, 2017 ABSTRACT Increasing efforts have been done to figure out the association between lncRNAs and complex diseases. Many computational models construct various lncRNA similarity networks, disease similarity networks, along with known lncRNA-disease associations to infer novel associations. However, most of them neglect the structural difference between lncRNAs network and diseases network, hierarchical relationships between diseases and pattern of newly discovered associations. In this study, we developed a model that performs B i- R andom W alks to predict novel L ncRNA- D isease A ssociations (BRWLDA in short). This model utilizes multiple heterogeneous data to construct the lncRNA functional similarity network, and Disease Ontology to construct a disease network. It then constructs a directed bi-relational network based on these two networks and available lncRNAs-disease associations. Next, it applies bi-random walks on the network to predict potential associations. BRWLDA achieves reliable and better performance than other comparing methods not only on experiment verified associations, but also on the simulated experiments with masked associations. Case studies further demonstrate the feasibility of BRWLDA in identifying new lncRNA-disease associations.