Prediction of miRNA-disease associations with a vector space model

TL;DR

This paper introduces a novel vector space model using distributional semantics to predict miRNA-disease associations, demonstrating high accuracy and potential for discovering new links and correcting false data.

Contribution

The study proposes a new high-dimensional vector space approach based on distributional semantics for miRNA-disease association prediction, outperforming existing methods.

Findings

High accuracy in cross-validation tests

Effective in discovering new miRNA-disease associations

Capable of identifying false associations in databases

Abstract

MicroRNAs play critical roles in many physiological processes. Their dysregulations are also closely related to the development and progression of various human diseases, including cancer. Therefore, identifying new microRNAs that are associated with diseases contributes to a better understanding of pathogenicity mechanisms. MicroRNAs also represent a tremendous opportunity in biotechnology for early diagnosis. To date, several in silico methods have been developed to address the issue of microRNA-disease association prediction. However, these methods have various limitations. In this study, we investigate the hypothesis that information attached to miRNAs and diseases can be revealed by distributional semantics. Our basic approach is to represent distributional information on miRNAs and diseases in a high-dimensional vector space and to define associations between miRNAs and diseases in terms of their vector similarity. Cross validations performed on a dataset of known miRNA-disease associations demonstrate the excellent performance of our method. Moreover, the case study focused on breast cancer confirms the ability of our method to discover new disease-miRNA associations and to identify putative false associations reported in databases.