Improved method for prioritization of disease associated lncRNAs based on ceRNA theory and functional genomics data.

ABSTRACT: Although several computational models that predict disease-associated lncRNAs (long non-coding RNAs) exist, only a limited number of disease-associated lncRNAs are known. In this study, we mapped lncRNAs to their functional genomics context using competing endogenous RNAs (ceRNAs) theory. Based on the criteria that similar lncRNAs are likely involved in similar diseases, we proposed a disease lncRNA prioritization method, DisLncPri, to identify novel disease-lncRNA associations. Using a leave-one-out cross validation (LOOCV) strategy, DisLncPri achieved reliable area under curve (AUC) values of 0.89 and 0.87 for the LncRNADisease and Lnc2Cancer datasets that further improved to 0.90 and 0.89 by integrating a multiple rank fusion strategy. We found that DisLncPri had the highest rank enrich

SUBMITTER: Wang P 

PROVIDER: S-EPMC5354861 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature