  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.

Quantitative Methods;Molecular Networks