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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.
By Claude Pasquier and Julien Gardès.
Scientific Reports 6, Article number: 27036 (2016) doi:10.1038/srep27036
Prediction of miRNA-disease associations with a vector space model