Detection of features predictive of microRNA targets by integration of network data.

ABSTRACT: Gene activity is controlled by multiple molecular mechanisms, for instance through transcription factors or by microRNAs (miRNAs), among others. Established bioinformatics tools for the prediction of miRNA target genes face the challenge of ensuring accuracy, due to high false positive rates. Further, these tools present poor overlap. However, we demonstrated that it is possible to filter good predictions of miRNA targets from the bulk of all predictions by using information from the gene regulatory network. Here, we take advantage of this strategy that selects a large subset of predicted microRNA binding sites as more likely to possess less false-positives because of their over-representation in RE1 silencing transcription factor (REST)-regulated genes from the background of TargetScanHum

SUBMITTER: Cihan M 

PROVIDER: S-EPMC9182691 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature