DRMDA: deep representations-based miRNA-disease association prediction.

Xing Chen,De‐Hong Zhang,Zhu‐Hong You,Yao Gong,Zheng‐Wei Li

doi:10.1111/jcmm.13336

Abstract

Recently, microRNAs (miRNAs) are confirmed to be important molecules within many crucial biological processes and therefore related to various complex human diseases. However, previous methods of predicting miRNA–disease associations have their own deficiencies. Under this circumstance, we developed a prediction method called deep representations‐based miRNA–disease association (DRMDA) prediction. The original miRNA–disease association data were extracted from HDMM database. Meanwhile, stacked auto‐encoder, greedy layer‐wise unsupervised pre‐training algorithm and support vector machine were implemented to predict potential associations. We compared DRMDA with five previous classical prediction models (HGIMDA, RLSMDA, HDMP, WBSMDA and RWRMDA) in global leave‐one‐out cross‐validation (LOOCV), local LOOCV and fivefold cross‐validation, respectively. The AUCs achieved by DRMDA were 0.9177, 08339 and 0.9156 ± 0.0006 in the three tests above, respectively. In further case studies, we predicted the top 50 potential miRNAs for colon neoplasms, lymphoma and prostate neoplasms, and 88%, 90% and 86% of the predicted miRNA can be verified by experimental evidence, respectively. In conclusion, DRMDA is a promising prediction method which could identify potential and novel miRNA–disease associations.

Highlights

MicroRNAs are a group of short non-coding RNAs (20– 25 nt) having important influence on post-transcription level of gene expression
To evaluate the effectiveness of deep representations-based miRNA–disease association (DRMDA), we introduced global leave-one-out cross-validation (LOOCV), local LOOCV and fivefold cross-validation
To fulfil the requirement of predicting potential miRNA–disease associations, we proposed a computational model called DRMDA

Summary

Introduction

MicroRNAs (miRNA) are a group of short non-coding RNAs (20– 25 nt) having important influence on post-transcription level of gene expression They bind to the 30 untranslated regions (UTRs) and repress target mRNA translation [1,2,3]. Shi et al [19] proposed a computational model using random walk algorithm on protein–protein interaction (PPI) network to predict new miRNA–disease associations. This model was based on the idea that one miRNA was more likely to be associated with a certain disease when it targeted genes which were related to that disease.

Methods

Results

Conclusion