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Investigation of Single and Multiple Mutations Prediction Using Binary Classification Approach

T. Edwin Ponraj1,*, J. Charles2

1 Department of Computer Applications, Noorul Islam Centre for Higher Education, Kumaracoil, 629180, India
2 Department of Software Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, 629180, India

* Corresponding Author: T. Edwin Ponraj. Email: email

Intelligent Automation & Soft Computing 2023, 36(1), 1189-1203. https://doi.org/10.32604/iasc.2023.033383

Abstract

The mutation is a critical element in determining the proteins’ stability, becoming a core element in portraying the effects of a drug in the pharmaceutical industry. Doing wet laboratory tests to provide a better perspective on protein mutations is expensive and time-intensive since there are so many potential mutations, computational approaches that can reliably anticipate the consequences of amino acid mutations are critical. This work presents a robust methodology to analyze and identify the effects of mutation on a single protein structure. Initially, the context in a collection of words is determined using a knowledge graph for feature selection purposes. The proposed prediction is based on an easier and simpler logistic regression inferred binary classification technique. This approach can able to obtain a classification accuracy (AUC) Area Under the Curve of 87% when randomly validated against experimental energy changes. Moreover, for each cross-fold validation, the precision, recall, and F-Score are presented. These results support the validity of our strategy since it performs the vast majority of prior studies in this domain.

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Cite This Article

APA Style
Ponraj, T.E., Charles, J. (2023). Investigation of single and multiple mutations prediction using binary classification approach. Intelligent Automation & Soft Computing, 36(1), 1189-1203. https://doi.org/10.32604/iasc.2023.033383
Vancouver Style
Ponraj TE, Charles J. Investigation of single and multiple mutations prediction using binary classification approach. Intell Automat Soft Comput . 2023;36(1):1189-1203 https://doi.org/10.32604/iasc.2023.033383
IEEE Style
T.E. Ponraj and J. Charles, “Investigation of Single and Multiple Mutations Prediction Using Binary Classification Approach,” Intell. Automat. Soft Comput. , vol. 36, no. 1, pp. 1189-1203, 2023. https://doi.org/10.32604/iasc.2023.033383



cc Copyright © 2023 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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