Large-scale prediction of microRNA-disease associations by combinatorial prioritization algorithm.

ABSTRACT: Identification of the associations between microRNA molecules and human diseases from large-scale heterogeneous biological data is an important step for understanding the pathogenesis of diseases in microRNA level. However, experimental verification of microRNA-disease associations is expensive and time-consuming. To overcome the drawbacks of conventional experimental methods, we presented a combinatorial prioritization algorithm to predict the microRNA-disease associations. Importantly, our method can be used to predict microRNAs (diseases) associated with the diseases (microRNAs) without the known associated microRNAs (diseases). The predictive performance of our proposed approach was evaluated and verified by the internal cross-validations and external independent validations based on s

SUBMITTER: Yu H 

PROVIDER: S-EPMC5357838 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature