Investigation of Single and Multiple Mutations Prediction Using Binary Classification Approach  

作　　者：T.Edwin Ponraj J.Charles 

机构地区：[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

出　　处：《Intelligent Automation & Soft Computing》2023年第4期1189-1203,共15页智能自动化与软计算（英文）

摘　　要：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 muta-tions,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 sim-pler 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.

关 键 词：PROTEINS data science mutation analysis random forest neighbor proteins single and double mutations 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]