Abstract
Accurately predicting Polyadenylation (Poly(A)) signals isthe key to understand the mechanism of translation regulation and mRNA metabolism. However, existing computational algorithms fail to work well for predicting Poly(A) signals due to the vanishing gradient problem when simply increasing the number of layers. In this work, we devise a spatiotemporal context-aware neural model called ACNet for Poly(A) signal prediction based on co-occurrence embedding. Specifically, genomic sequences of Poly(A) signals are first split into k-mer sequences, and k-mer embeddings are pre-trained based on the co-occurrence matrix information; Then, gated residual networks are devised to fully extract spatial information, which has an excellent ability to control the information flow and ease the problem of vanishing gradients. The gated mechanism generates channel weights by a dilated convolution and aggregates local features by identity connections which are obtained by multi-scale dilated convolutions. Experimental results indicate that our ACNet model outperforms the state-of-the-art prediction methods on various Poly(A) signal data, and an ablation study shows the effectiveness of the design strategy.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.