Integrating random walk and binary regression to identify novel miRNA-disease association

Ya-Wei Niu,Xing Chen,Gui-Ying Yan,Guang-Hui Wang

doi:10.1186/s12859-019-2640-9

Ya-Wei Niu, Xing Chen + Show 2 more

Open Access

https://doi.org/10.1186/s12859-019-2640-9

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Abstract

BackgroundIn the last few decades, cumulative experimental researches have witnessed and verified the important roles of microRNAs (miRNAs) in the development of human complex diseases. Benefitting from the rapid growth both in the availability of miRNA-related data and the development of various analysis methodologies, up until recently, some computational models have been developed to predict human disease related miRNAs, efficiently and quickly.ResultsIn this work, we proposed a computational model of Random Walk and Binary Regression-based MiRNA-Disease Association prediction (RWBRMDA). RWBRMDA extracted features for each miRNA from random walk with restart on the integrated miRNA similarity network for binary logistic regression to predict potential miRNA-disease associations. RWBRMDA obtained AUC of 0.8076 in the leave-one-out cross validation. Additionally, we carried out three different patterns of case studies on four human complex diseases. Specifically, Esophageal cancer and Prostate cancer were conducted as one kind of case study based on known miRNA-disease associations in HMDD v2.0 database. Out of the top 50 predicted miRNAs, 94 and 90% were respectively confirmed by recent experimental reports. To simulate new disease without known related miRNAs, the information of known Breast cancer related miRNAs was removed. As a result, 98% of the top 50 predicted miRNAs for Breast cancer were confirmed. Lymphoma, the verified ratio of which was 88%, was used to assess the prediction robustness of RWBRMDA based on the association records in HMDD v1.0 database.ConclusionsWe anticipated that RWBRMDA could benefit the future experimental investigations about the relation between human disease and miRNAs by generating promising and testable top-ranked miRNAs, and significantly reducing the effort and cost of identification works.

Highlights

In the last few decades, cumulative experimental researches have witnessed and verified the important roles of microRNAs in the development of human complex diseases
Receiver operating characteristics (ROC) curve could be drawn by plotting the true positive rate (TPR) versus the false positive rate (FPR) at different thresholds
Identifying potential miRNA-disease associations was vitally important for investigating the biomarker of disease diagnosis at the miRNA level

Summary

Introduction

In the last few decades, cumulative experimental researches have witnessed and verified the important roles of microRNAs (miRNAs) in the development of human complex diseases. Benefitting from the rapid growth both in the availability of miRNA-related data and the development of various analysis methodologies, up until recently, some computational models have been developed to predict human disease related miRNAs, efficiently and quickly. It’s meaningful and uncontroversial to regard disease-related miRNAs as potential biomarkers, which could significantly contribute to comprehending the diseases mechanisms, and benefit the detection, prognosis, diagnosis, treatment and prevention of human complex diseases [24,25,26,27]. Considering the massive increases in the reliability and volume of miRNA-related data based on the accumulated researches about miRNAs, it became necessary and doable to develop effective computational models for predicting potential miRNA-disease associations, which could further enhance the understanding of disease development in miRNA level. The promising prediction results of computational approaches could offer convenience for the follow-up validation experiment by biologic or biomedical researchers [28, 29]

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