Issue |
BIO Web Conf.
Volume 75, 2023
The 5th International Conference on Bioinformatics, Biotechnology, and Biomedical Engineering (BioMIC 2023)
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|
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Article Number | 01004 | |
Number of page(s) | 7 | |
Section | Bioinformatics and Data Mining | |
DOI | https://doi.org/10.1051/bioconf/20237501004 | |
Published online | 15 November 2023 |
Exploring Reinforcement Learning Methods for Multiple Sequence Alignment: A Brief Review
1 LISA Laboratory, National School of Applied Sciences, University of Cadi Ayyad, Marrakech, Morocco
2 LISI Laboratory, Computer science department, Faculty of Sciences Semlalia, University of Cadi Ayyad, Marrakech, Morocco
3 MSC Laboratory, National school of applied sciences, University of Cadi Ayyad, Marrakech, Morocco
* Corresponding author: chaimaa.gaad@ced.uca.ma
Multiple sequence alignment (MSA) plays a vital role in uncovering similarities among biological sequences such as DNA, RNA, or proteins, providing valuable information about their structural, functional, and evolutionary relationships. However, MSA is a computationally challenging problem, with complexity growing exponentially as the number and length of sequences increase. Currently, standard MSA tools like ClustalW, T-Coffee, and MAFFT, which are based on heuristic algorithms, are widely used but still face many challenges due to the combinatorial explosion. Recent advancements in MSA algorithms have employed reinforcement learning (RL), particularly deep reinforcement learning (DRL), and demonstrated optimized execution time and accuracy with promising results. This is because deep reinforcement learning algorithms update their search policies using gradient descent, instead of exploring the entire solution space making it significantly faster and efficient. In this article, we provide an overview of the recent historical advancements in MSA algorithms, highlighting RL models used to tackle the MSA problem and main challenges and opportunities in this regard.
Key words: Multiple sequence alignment / Reinforcement learning / Computational complexity / Bioinformatics / Brief review
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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