Inferring miRNA-disease associations using collaborative filtering and resource allocation on a tripartite graph

Van Tinh Nguyen,Thi Tu Kien Le,Tran Quoc Vinh Nguyen,Dang Hung Tran

doi:10.1186/s12920-021-01078-8

Abstract

BackgroundDeveloping efficient and successful computational methods to infer potential miRNA-disease associations is urgently needed and is attracting many computer scientists in recent years. The reason is that miRNAs are involved in many important biological processes and it is tremendously expensive and time-consuming to do biological experiments to verify miRNA-disease associations.MethodsIn this paper, we proposed a new method to infer miRNA-disease associations using collaborative filtering and resource allocation algorithms on a miRNA-disease-lncRNA tripartite graph. It combined the collaborative filtering algorithm in CFNBC model to solve the problem of imbalanced data and the method for association prediction established multiple types of known associations among multiple objects presented in TPGLDA model.ResultsThe experimental results showed that our proposed method achieved a reliable performance with Area Under Roc Curve (AUC) and Area Under Precision-Recall Curve (AUPR) values of 0.9788 and 0.9373, respectively, under fivefold-cross-validation experiments. It outperformed than some other previous methods such as DCSMDA and TPGLDA. Furthermore, it demonstrated the ability to derive new associations between miRNAs and diseases among 8, 19 and 14 new associations out of top 40 predicted associations in case studies of Prostatic Neoplasms, Heart Failure, and Glioma diseases, respectively. All of these new predicted associations have been confirmed by recent literatures. Besides, it could discover new associations for new diseases (or miRNAs) without any known associations as demonstrated in the case study of Open-angle glaucoma disease.ConclusionWith the reliable performance to infer new associations between miRNAs and diseases as well as to discover new associations for new diseases (or miRNAs) without any known associations, our proposed method can be considered as a powerful tool to infer miRNA-disease associations.

Highlights

Developing efficient and successful computational methods to infer potential miRNA-disease associations is urgently needed and is attracting many computer scientists in recent years
To solve the sparsity data problem and to take advantages of the integration of multiple types of known associations among multiple objects in improving prediction accuracy, in this paper, we proposed a new method to infer miRNA-disease associations using collaborative filtering and resource allocation algorithms on a tripartite graph
We computed the false positive rate (FPR) and true positive rate (TPR) with different γ values where FPR indicates the proportion of the real negative samples in predicted positive samples to all negative samples and TPR indicates the proportion of the real positive samples in all predicted positive samples

Summary

Introduction

Developing efficient and successful computational methods to infer potential miRNA-disease associations is urgently needed and is attracting many computer scientists in recent years. The reason is that miRNAs are involved in many important biological processes and it is tremendously expensive and time-consuming to do biological experiments to verify miRNA-disease associations. There are only a few known miRNA-disease associations in comparison with the number of newly discovered miRNAs. Until now, there are only a few known miRNA-disease associations in comparison with the number of newly discovered miRNAs It is tremendously expensive and time-consuming to do biological experiments to verify miRNA-disease associations. Expanding effective and outstanding computational methods to predict potential miRNAdisease associations is urgently needed and is attracting many computer scientists in recent years [7]

Methods

Results

Conclusion