MSF-UBRW: An Improved Unbalanced Bi-Random Walk Method to Infer Human lncRNA-Disease Associations

Abstract

Long-non-coding RNA (lncRNA) is a transcription product that exerts its biological functions through a variety of mechanisms. The occurrence and development of a series of human diseases are closely related to abnormal expression levels of lncRNAs. Scientists have developed many computational models to identify the lncRNA-disease associations (LDAs). However, many potential LDAs are still unknown. In this paper, a novel method, namely MSF-UBRW (multiple similarities fusion based on unbalanced bi-random walk), is designed to explore new LDAs. First, two similarities (functional similarity and Gaussian Interaction Profile kernel similarity) of lncRNAs are calculated and fused linearly, also for disease data. Then, the known association matrix is preprocessed. Next, the linear neighbor similarities of lncRNAs and diseases are calculated, respectively. After that, the potential associations are predicted based on unbalanced bi-random walk. The fusion of multiple similarities improves the prediction performance of MSF-UBRW to a large extent. Finally, the prediction ability of the MSF-UBRW algorithm is measured by two statistical methods, leave-one-out cross-validation (LOOCV) and 5-fold cross-validation (5-fold CV). The AUCs of 0.9391 in LOOCV and 0.9183 (±0.0054) in 5-fold CV confirmed the reliable prediction ability of the MSF-UBRW method. Case studies of three common diseases also show that the MSF-UBRW method can infer new LDAs effectively.

A Review of Random Walk-Based Method for the Identification of Disease Genes and Disease Modules

“…To compute the potential association scores between disease and its associated miRNAs, it takes three different random walking steps on lncRNA similarity network, disease similarity network, and miRNA similarity network for miRNA-disease association prediction. Multiple Similarities Fusion based on Unbalanced Bi-Random Walk (MSF-UBRW) [73] is based on a multiple similarities fusion of an unbalanced bi-random walk used to identify lncRNA-disease associations. This method fuses multiple similarities (including functional, Gaussian Interaction Profile Kernel, and linear neighbour similarities) of lncRNAs and diseases to assist different random walking steps for the lncRNA and disease similarity networks, respectively.…”

“…Long-non-coding RNAs (lncRNAs) are long chains of nucleotides with various biological mechanisms closely related to human diseases, including cancers and degenerative neurological diseases [72]. Based on the hypothesis that functionally similar lncRNAs are possibly related to diseases with similar phenotypes [73], lncRNA-disease association prediction has rapidly gained attention among researchers in understanding the pathogenesis of diseases at a molecular level. By integrating multiple biological data sources, random walk models can effectively integrate disease semantic similarity networks and lncRNA function similarity networks with known lncRNA-disease associations to predict lncRNAdisease associations.…”

Predicting lncRNA–Disease Associations Based on a Dual-Path Feature Extraction Network with Multiple Sources of Information Integration

“…LRWRHLDA, Laplace normalization of similarities and interactions between diseases, genes and noncoding RNAs, can be used to make a final prediction after several rounds of iterative training, and a similar method is MHRWR . Birandom walks are also available in MSF-UBRW, where the interaction between lncRNAs and diseases was reconstructed using WKNKN based on unbalanced birandom walks and used as a transfer matrix for the respective networks. Similar birandom walk methods are used in NCP-BiRW and lung cancer prediction .…”