Comprehensive machine-learning-based analysis of microRNA-target interactions reveals variable transferability of interaction rules across species.

ABSTRACT:

Background

MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression post-transcriptionally via base-pairing with complementary sequences on messenger RNAs (mRNAs). Due to the technical challenges involved in the application of high-throughput experimental methods, datasets of direct bona fide miRNA targets exist only for a few model organisms. Machine learning (ML)-based target prediction models were successfully trained and tested on some of these datasets. There is a need to further apply the trained models to organisms in which experimental training data are unavailable. However, it is largely unknown how the features of miRNA-target interactions evolve and whether some features have remained fixed during evolution, raising questions regarding the general, cros

SUBMITTER: Ben Or G 

PROVIDER: S-EPMC8146624 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature