Benchmark of computational methods for predicting microRNA-disease associations.

ABSTRACT:

Background

A series of miRNA-disease association prediction methods have been proposed to prioritize potential disease-associated miRNAs. Independent benchmarking of these methods is warranted to assess their effectiveness and robustness.

Results

Based on more than 8000 novel miRNA-disease associations from the latest HMDD v3.1 database, we perform systematic comparison among 36 readily available prediction methods. Their overall performances are evaluated with rigorous precision-recall curve analysis, where 13 methods show acceptable accuracy (AUPRC > 0.200) while the top two methods achieve a promising AUPRC over 0.300, and most of these methods are also highly ranked when considering only the causal miRNA-disease associations as the positive samples. The potential of perf

SUBMITTER: Huang Z 

PROVIDER: S-EPMC6781296 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature