TY  - EJOU
AU  - Ponraj, T. Edwin 
AU  - Charles, J. 

TI  - Investigation of Single and Multiple Mutations Prediction Using Binary Classification Approach
T2  - Intelligent Automation \& Soft Computing

PY  - 2023
VL  - 36
IS  - 1
SN  - 2326-005X

AB  - 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.
KW  - Proteins; data science; mutation analysis; random forest; neighbor proteins; single and double mutations

DO  - 10.32604/iasc.2023.033383