首页|Analysis of Protein Structure for Drug Repurposing Using Computational Intelligence and ML Algorithm
Analysis of Protein Structure for Drug Repurposing Using Computational Intelligence and ML Algorithm
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NETL
NSTL
Igi Global
Proteins are fundamental compounds in biological processes during the analysis of drug target indication for drug repurposing. The identification of relevant features is a necessary step in determining protein structure. A classification technique is used to identify the most important features in a dataset, which is why feature selection is so important. For protein structure prediction, recent research has developed a wide range of new methods to improve accuracy. The authors use principal component analysis (PCA) with correlation-matrix-based feature selection to analyse breast cancer data. In this paper, they discussed a therapeutic agent that is used to reduce the dataset by reduction-based algorithm and after that applied reduced dataset labelled as Standard Gold Dataset on machine learning model to analyze drug target indication. They get the higher accuracy of 92.8%, 93.9%, and 95.3%, each of the three datasets with 200, 500, and 1000 features with SVM with RBF kernel function. Also they found the best result, 97.8%, with the same classifier.
Drug Target InteractionFeature SelectionMachine Learning ClassifierPrinciple Component AnalysisProtein Sequence
NETL
NSTL
Igi Global
