Biased Random Walk With Restart on Multilayer Heterogeneous Networks for MiRNA-Disease Association Prediction.

Wen-Di Zhao,Jia Qu,Shu-Bin Cai,Xiao-Long Cheng,Chun-Chun Wang,Zhong Ming

doi:10.3389/fgene.2021.720327

Abstract

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 the medical field. Here, based on heterogeneous networks built on the information of known miRNA–disease associations, miRNA function similarity, disease semantic similarity, and 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 an AUC 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 the case study all showed that the proposed model has good and stable performance.

Highlights

MicroRNA is a noncoding single-stranded RNA with a length of about 22 nucleotides and pervasive in both animals and plants (Axtell et al, 2011)
We employed BRWRMHMDA to infer candidate miRNAs for esophageal neoplasms in the light of known miRNA–disease associations extracted from HMDD v2.0 (Li et al, 2014) and implemented the model to predict breast neoplasms-associated miRNAs on the basis of known miRNA–disease associations collected from HMDAD v1.0
In order to analyze the performance of BRWRMHMDA, the proposed method has been extensively compared with some classic algorithms (ELLPMDA, inductive matrix completion for miRNA–disease association prediction (IMCMDA), EGBMMDA, MDHGI, TLHNMDA, MaxFlow, regularized least squares for miRNA–disease association (RLSMDA), human disease-related miRNA prediction (HDMP), WBSMDA, MirAI, and miRNAs associated with diseases prediction (MIDP)) based on the 5,430 known miRNA–disease associations from the HMDD v2.0 database (Li et al, 2014) via local leave-one-out crossvalidation (LOOCV)

Summary

Introduction

MicroRNA (miRNA) is a noncoding single-stranded RNA with a length of about 22 nucleotides and pervasive in both animals and plants (Axtell et al, 2011). MiRNAs have potential influences on almost all genetic pathways, and the upregulation and downregulation of miRNA expression in the human body are correlated to various complex diseases (Liu et al, 2008). Using traditional experiment approach to identify potential miRNA-disease associations is usually complex, time consuming and expensive. It is an urgent need for scholars to develop calculation models to predict new miRNA–disease associations. We expect that miRNA–disease pairs with high scores could be selected for experimental verification, which would significantly reduce the time and cost of biological experiments

Methods

Results

Conclusion