DOI: 10.1101/463976Nov 7, 2018Paper

Rationalizing Translation Elongation by Reinforcement Learning

BioRxiv : the Preprint Server for Biology
Hailin HuJianyang Zeng

Abstract

Translation elongation plays a crucial role in multiple aspects of protein biogenesis. In this study, we develop a novel deep reinforcement learning based framework, named RiboRL, to model the distributions of ribosomes on transcripts. In particular, RiboRL employs a policy network (PolicyNet) to perform a context-dependent feature selection to facilitate the prediction of ribosome density. Extensive tests demonstrate that RiboRL can outperform other state-of-the-art methods in predicting ribosome densities. We also show that the reinforcement learning based strategy can generate more informative features for the prediction task when compared to other commonly used attribution methods in deep learning. Moreover, the in-depth analyses and a case study also indicate the potential applications of the RiboRL framework in generating meaningful biological insights regarding translation elongation dynamics. These results have established RiboRL as a useful computational tool to facilitate the studies of the underlying mechanisms of translational regulation.

Software Mentioned

RiboShape
iχnos
PolicyNet
DeepLift
iXnos
RUST
RiboRL
REINFORCE
BiRNN
hisat2

Related Concepts

Related Feeds

BioRxiv & MedRxiv Preprints

BioRxiv and MedRxiv are the preprint servers for biology and health sciences respectively, operated by Cold Spring Harbor Laboratory. Here are the latest preprint articles (which are not peer-reviewed) from BioRxiv and MedRxiv.

Related Papers

BioRxiv : the Preprint Server for Biology
B. J. Fremin, Ami S Bhatt
Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Rania IbrahimNagwa M El-Makky
© 2021 Meta ULC. All rights reserved