Prediction of miRNA-disease associations with a vector space model.

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 d

SUBMITTER: Pasquier C 

PROVIDER: S-EPMC4887905 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature