BRWMC: Predicting lncRNA-disease associations based on bi-random walk and matrix completion on disease and lncRNA networks. (April 2023)

• Record Type: Journal Article
• Authors: Zhang, Guo-Zheng; Gao, Ying-Lian
• Is Part Of: Computational biology and chemistry. Volume 103(2023)
• Publication Date: 2023-04
• Subjects: Random walk -- LncRNA-disease association prediction -- Similarity network fusion -- Matrix completion; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2023.107833
• Languages: English
• ISSNs: 1476-9271

Abstract

Abstract: Many experiments have proved that long non-coding RNAs (lncRNAs) in humans have been implicated in disease development. The prediction of lncRNA-disease association is essential in promoting disease treatment and drug development. It is time-consuming and laborious to explore the relationship between lncRNA and diseases in the laboratory. The computation-based approach has clear advantages and has become a promising research direction. This paper proposes a new lncRNA disease association prediction algorithm BRWMC. Firstly, BRWMC constructed several lncRNA (disease) similarity networks based on different measurement angles and fused them into an integrated similarity network by similarity network fusion (SNF). In addition, the random walk method is used to preprocess the known lncRNA-disease association matrix and calculate the estimated scores of potential lncRNA-disease associations. Finally, the matrix completion method accurately predicts the potential lncRNA-disease associations. Under the framework of leave-one-out cross-validation and 5-fold cross-validation, the AUC values obtained by BRWMC are 0.9610 and 0.9739, respectively. In addition, case studies of three common diseases show that BRWMC is a reliable method for prediction. Graphical abstract: Flowchart of BRWMC: Part A: The lncRNA functional similarity matrix (LF ) and lncRNA Gaussian kernel similarity matrix (LG ) are fused by SNF. Part B: The disease semantic similarity matrix (DS ) and disease Gaussian kernel similarity matrix (DG ) are fused by SNF. Part C: The random walk method is used to preprocess the known association matrix Y, and the matrix completion method is used to predict the association of lncRNA and disease. ga1 Highlights: The lncRNAs (diseases) without any known association have learned new association information through the random walk method. Many potential lncRNA-disease associations are mined by the random walk method. The expression of the lncRNA-disease association strength in the known association matrix is more accurate. BRWMC has constructed multiple lncRNA similarity networks and disease similarity networks in the preparation stage.