REVIEW ARTICLE


A Study of Bio-inspired Computing in Bioinformatics: A State-of-the-art Literature Survey



Akshaya Kumar Mandal1, *, Pankaj Kumar Deva Sarma1, Satchidananda Dehuri2
1 Department of Computer Science, Assam University, A Central University of India Assam, Silchar 788011, Assam, India
2 Department of Computer Science, Fakir Mohan University, Vyasa Vihar, Balasore 756019, Odisha, India


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Creative Commons License
© 2023 Mandal et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Department of Computer Science, Assam University, A Central University of India, Assam, Silchar 788011, Assam, India; E-mail: akshayacs207@gmail.com


Abstract

Background:

Bioinspired computing algorithms are population-based probabilistic search optimization approaches inspired by biological evolution and activity. These are highly efficient and can solve several problems based on human, chimpanzee, bird, and insect behavior. These approaches have been proposed by the scientific community over the last two decades for common application to solving bioinformatics design problems.

Materials and Methodology:

The advanced search boxes in databases such as PubMed, WoS, Science Direct, IEEE Xplore, and Scopus to conduct this research. Keywords such as "machine learning," "bioinspired computing," "DNA sequence optimization," and "bioinformatics" were used with OR and AND operators. Journal and conference articles were the two types of articles focused on, and other reports and book chapters were removed using the search engine's parameters.

Results:

Bioinspired techniques are becoming increasingly popular in computer science, electrical engineering, applied mathematics, aeronautical engineering, and bioinformatics. Parametric comparisons suggest that most classic benchmark approaches can be successfully used by employing bioinspired techniques. 56 % of studies are modification based, 30 % hybrid based, and 14 % multiobjective based.

Conclusion:

These algorithms can be used to optimize data sets in bioinformatics due to their capacity to solve real-world challenges and their ability to accurately express sequence quality and evaluate DNA sequence optimization.

Keywords: Bioinspired computing, Machine Learning (ML), Bioinformatics, DNA sequencing, Deep Learning (DL), Algorithms.