CircCNNs, a convolutional neural network framework to better understand the biogenesis of exonic circRNAs.

ABSTRACT: Circular RNAs (circRNAs) as biomarkers for cancer detection have been extensively explored, however, the biogenesis mechanism is still elusive. In contrast to linear splicing (LS) involved in linear transcript formation, the so-called back splicing (BS) process has been proposed to explain circRNA formation. To investigate the potential mechanism of BS via the machine learning approach, we curated a high-quality BS and LS exon pairs dataset with evidence-based stringent filtering. Two convolutional neural networks (CNN) base models with different structures for processing splicing junction sequences including motif extraction were created and compared after extensive hyperparameter tuning. In contrast to the previous study, we are able to identify motifs corresponding to well-established B

SUBMITTER: Wang C 

PROVIDER: S-EPMC11329666 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature