RKNNMDA: Ranking-based KNN for MiRNA-Disease Association prediction.

ABSTRACT: Cumulative verified experimental studies have demonstrated that microRNAs (miRNAs) could be closely related with the development and progression of human complex diseases. Based on the assumption that functional similar miRNAs may have a strong correlation with phenotypically similar diseases and vice versa, researchers developed various effective computational models which combine heterogeneous biologic data sets including disease similarity network, miRNA similarity network, and known disease-miRNA association network to identify potential relationships between miRNAs and diseases in biomedical research. Considering the limitations in previous computational study, we introduced a novel computational method of Ranking-based KNN for miRNA-Disease Association prediction (RKNNMDA) to predict

SUBMITTER: Chen X 

PROVIDER: S-EPMC5546566 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature