Biased Random Walk With Restart on Multilayer Heterogeneous Networks for MiRNA–Disease Association Prediction - Qu, Jia; Wang, Chun-Chun; Cai, Shu-Bin; Zhao, Wen-Di; Cheng, Xiao-Long; Ming, Zhong (Frontiers in Genetics, 2021)
Numerous experiments have proved that microRNAs (miRNAs) could be used as diagnostic biomarkers for many complex diseases. Thus, it is conceivable that predicting the unobserved associations between miRNAs and diseases is extremely significant for medical field. Here, based on heterogeneous networks built on the information of known miRNA-disease associations, miRNA function similarity, disease semantic similarity, Gaussian interaction profile kernel similarity for miRNAs and diseases, we developed a computing model of Biased Random Walk with Restart on Multilayer Heterogeneous networks for MiRNA-Disease Association prediction (BRWRMHMDA) through enforcing degree-based biased random walk with restart (BRWR). Assessment results reflected that AUCs of 0.8310 was gained in local leave-one-out cross-validation (LOOCV), which proved the calculation algorithm’s good performance. Besides, we carried out BRWRMHMDA to prioritize candidate miRNAs for esophageal neoplasms based on HMDD v2.0. We further prioritize candidate miRNAs for breast neoplasms based on HMDD v1.0. The local LOOCV results and performance analysis of case study all showed that the proposed model has good and stable performance.