Revolutionizing Genomics with Reinforcement Learning Techniques

• URL: http://arxiv.org/abs/2302.13268v6
• Date: Sun, 22 Dec 2024 03:21:48 GMT
• Title: Revolutionizing Genomics with Reinforcement Learning Techniques
• Authors: M. Karami, K. Jahanian, R. Alizadehsani, A. Argha, I. Dehzangi, J. M. Gorriz, Y. Zhang, F. Hajati, M. Yang, H. Alinejad-Rokny,
• Abstract summary: Reinforcement Learning (RL) has emerged as a powerful tool for solving a wide range of problems. RL algorithms are capable of learning from experience with minimal human supervision. One of the key benefits of using RL is the reduced cost associated with collecting labeled training data.
• Score: 0.2122194064694661
• License:
• Abstract: In recent years, Reinforcement Learning (RL) has emerged as a powerful tool for solving a wide range of problems, including decision-making and genomics. The exponential growth of raw genomic data over the past two decades has exceeded the capacity of manual analysis, leading to a growing interest in automatic data analysis and processing. RL algorithms are capable of learning from experience with minimal human supervision, making them well-suited for genomic data analysis and interpretation. One of the key benefits of using RL is the reduced cost associated with collecting labeled training data, which is required for supervised learning. While there have been numerous studies examining the applications of Machine Learning (ML) in genomics, this survey focuses exclusively on the use of RL in various genomics research fields, including gene regulatory networks (GRNs), genome assembly, and sequence alignment. We present a comprehensive technical overview of existing studies on the application of RL in genomics, highlighting the strengths and limitations of these approaches. We then discuss potential research directions that are worthy of future exploration, including the development of more sophisticated reward functions as RL heavily depends on the accuracy of the reward function, the integration of RL with other machine learning techniques, and the application of RL to new and emerging areas in genomics research. Finally, we present our findings and conclude by summarizing the current state of the field and the future outlook for RL in genomics.

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