Open Access

ARTICLE

Investigation of Single and Multiple Mutations Prediction Using Binary Classification Approach

T. Edwin Ponraj^1,*, 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:

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

Received 15 June 2022; Accepted 26 July 2022; Issue published 29 September 2022

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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Keywords

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