Nov 7, 2018

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 have developed a novel deep reinforcement learning based framework, named RiboRL, to model the distribution of ribosomes on transcripts. In particular, RiboRL depends on the policy network (PolicyNet) to perform a context-dependent feature selection to facilitate the prediction of ribosome density. Extensive tests have shown that RiboRL can outperform the state-of-the-art methods in predicting ribosome density. We have also shown 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 biological insights regarding translation elongation dynamics. These results have established RiboRL as a useful computational tool to enhance the current understanding of the underlying mechanisms of translation regulation.

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Mentioned in this Paper

Study
Translational Elongation
Psychological Reinforcement
Attribution
Ribosomes
Learning
Analysis
Reinforcement Surgical Repair
Case-Control Studies
Anabolism

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