Weakly supervised learning of RNA modifications from low-resolution epitranscriptome data.

ABSTRACT:

Motivation

Increasing evidence suggests that post-transcriptional ribonucleic acid (RNA) modifications regulate essential biomolecular functions and are related to the pathogenesis of various diseases. Precise identification of RNA modification sites is essential for understanding the regulatory mechanisms of RNAs. To date, many computational approaches for predicting RNA modifications have been developed, most of which were based on strong supervision enabled by base-resolution epitranscriptome data. However, high-resolution data may not be available.

Results

We propose WeakRM, the first weakly supervised learning framework for predicting RNA modifications from low-resolution epitranscriptome datasets, such as those generated from acRIP-seq and hMeRIP-seq. Evaluations on th

SUBMITTER: Huang D 

PROVIDER: S-EPMC8336446 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature