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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.


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.


Cite This Article

T. Edwin Ponraj and J. Charles, "Investigation of single and multiple mutations prediction using binary classification approach," Intelligent Automation & Soft Computing, vol. 36, no.1, pp. 1189–1203, 2023.

cc 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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