Abstract

Recently, researchers have been increasingly focusing on microRNAs (miRNAs) with accumulating evidence indicating that miRNAs serve as a vital role in various biological processes and dysfunctions of miRNAs are closely related with human complex diseases. Predicting potential associations between miRNAs and diseases is attached considerable significance in the domains of biology, medicine, and bioinformatics. In this study, we developed a computational model of Personalized Recommendation-based MiRNA-Disease Association prediction (PRMDA) to predict potential related miRNA for all diseases by implementing personalized recommendation-based algorithm based on integrated similarity for diseases and miRNAs. PRMDA is a global method capable of prioritizing candidate miRNAs for all diseases simultaneously. Moreover, the model could be applied to diseases without any known associated miRNAs. PRMDA obtained AUC of 0.8315 based on leave-one-out cross validation, which demonstrated that PRMDA could be regarded as a reliable tool for miRNA-disease association prediction. Besides, we implemented PRMDA on the HMDD V1.0 and HMDD V2.0 databases for three kinds of case studies about five important human cancers in order to test the performance of the model from different perspectives. As a result, 92%, 94%, 88%, 96% and 88% out of the top 50 candidate miRNAs predicted by PRMDA for Colon Neoplasms, Esophageal Neoplasms, Lymphoma, Lung Neoplasms and Breast Neoplasms, respectively, were confirmed by experimental reports.

Highlights

  • Discovered in Caenorhabditis elegans at first, microRNAs are a highly profuse class of short, with length of 21–24 nucleotides, endogenous singlestranded non-coding RNAs [1, 2]

  • We developed a computational model of Personalized Recommendation-based MiRNA-Disease Association prediction (PRMDA) to predict potential related miRNA for all diseases by implementing personalized recommendation-based algorithm based on integrated similarity for diseases and miRNAs

  • In order to evaluate the prediction accuracy of PRMDA, we implemented leave-one-out cross validation (LOOCV) based on verified miRNA-disease associations recorded in the HMDD V2.0 database [48], along with performance comparison among five advanced computational approaches for miRNA-disease association prediction: HGIMDA [28], Regularized Least Squares for MiRNA-Disease Association (RLSMDA) [45], HDMP [30], WBSMDA [29], and Random Walk with Restart for MiRNADisease Association (RWRMDA) [38]

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Summary

Introduction

Discovered in Caenorhabditis elegans at first, microRNAs (miRNAs) are a highly profuse class of short, with length of 21–24 nucleotides, endogenous singlestranded non-coding RNAs (ncRNAs) [1, 2]. Taking a considerable number of biological databases related with miRNA into consideration, developing innovative and efficient computational models to identify possible miRNA-disease associations is urgently required. There is no doubt that prioritizing related-diseases and related-miRNAs for newly discovered miRNAs and diseases could effectively contribute to promoting disease biomarker detection for the prevention, diagnosis and treatment of human diseases [22]. It is considered as a critical function for a method of identifying miRNA-disease associations

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