@Article{iasc.2023.033383,
AUTHOR = {T. Edwin Ponraj, J. Charles},
TITLE = {Investigation of Single and Multiple Mutations Prediction Using Binary Classification Approach},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {36},
YEAR = {2023},
NUMBER = {1},
PAGES = {1189--1203},
URL = {http://www.techscience.com/iasc/v36n1/50035},
ISSN = {2326-005X},
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.},
DOI = {10.32604/iasc.2023.033383}
}