Inferring microRNA-disease association by hybrid recommendation algorithm and unbalanced bi-random walk on heterogeneous network

Abstract

More and more research works have indicated that microRNAs (miRNAs) play indispensable roles in exploring the pathogenesis of diseases. Detecting miRNA-disease associations by experimental techniques in biology is expensive and time-consuming. Hence, it is important to propose reliable and accurate computational methods to exploring potential miRNAs related diseases. In our work, we develop a novel method (BRWHNHA) to uncover potential miRNAs associated with diseases based on hybrid recommendation algorithm and unbalanced bi-random walk. We first integrate the Gaussian interaction profile kernel similarity into the miRNA functional similarity network and the disease semantic similarity network. Then we calculate the transition probability matrix of bipartite network by using hybrid recommendation algorithm. Finally, we adopt unbalanced bi-random walk on the heterogeneous network to infer undiscovered miRNA-disease relationships. We tested BRWHNHA on 22 diseases based on five-fold cross-validation and achieves reliable performance with average AUC of 0.857, which an area under the ROC curve ranging from 0.807 to 0.924. As a result, BRWHNHA significantly improves the performance of inferring potential miRNA-disease association compared with previous methods. Moreover, the case studies on lung neoplasms and prostate neoplasms also illustrate that BRWHNHA is superior to previous prediction methods and is more advantageous in exploring potential miRNAs related diseases. All source codes can be downloaded from https://github.com/myl446/BRWHNHA .

iMDA-BN: Identification of miRNA-disease associations based on the biological network and graph embedding algorithm

“… 5 This method is reported in [41] . 6 This method is reported in [42] . 7 This method is reported in [13] .…”

A many‐objective optimization‐based local tensor factorization model for skin cancer detection

“…Li et al 19 used a restarted double random walk with network projection to predict the relationship between miRNAs and diseases. Yu et al 23 incorporated Gaussian interaction profile nuclear similarity into other biological similarities auxiliary information and then used an unbalanced double random walk with network projection technique to predict the relationship between miRNAs and diseases. Qu et al 24 predict miRNA‐disease association by enforcing degree‐based biased random walk on a multi‐layer heterogeneous network, which is constructed using known miRNA‐disease association information.…”

DEJKMDR: miRNA-disease association prediction method based on graph convolutional network

“…Logistic profile-weighted bi-random walk was suggested by Dai et al ( 29 ) to explore miRNA-disease associations. An amalgamated ranking algorithm and a disproportionate bi-random walk on a network with heterogeneity were developed by Yu et al ( 30 ) to infer microRNA-disease association. Biased Random Exercises with Restart on Multilayer Hierarchical Networks was conducted by Qu et al ( 31 ) to conduct miRNA–Disease Association prediction.…”