Revolutionizing Genomics with Reinforcement Learning Techniques

By Mohsen Karami et al

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Table of Contents

Abstract
Keywords
I. Introduction
II. Reinforcement Learning
A. Value-based methods
B. Policy search
C. RL Challenges
III. Application of Reinforcement Learning in Genomics
A. Gene regulatory networks
B. Genome Assembly
C. Sequence Alignment
IV. Conclusion
References

Summary

In recent years, Reinforcement Learning (RL) has emerged as a powerful tool for solving a wide range of problems, including decision-making and genomics. This paper focuses exclusively on the use of RL in various genomics research fields, highlighting the strengths and limitations of these approaches. Various methods of RL, including value-based and policy search methods, are discussed, along with the challenges faced in applying RL in genomics. The application of RL in gene regulatory networks, genome assembly, and sequence alignment is explored, showcasing the potential of RL in advancing genomics research.