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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

Deepak Srivastava, Kwok Tai Chui, Varsha Arya, Francisco José García Peñalvo, Pramod Kumar, Anuj Kumar Singh
Copyright: © 2022 |Volume: 14 |Issue: 1 |Pages: 11
ISSN: 1942-9045|EISSN: 1942-9037|EISBN13: 9781683181019|DOI: 10.4018/IJSSCI.312562
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MLA

Srivastava, Deepak, et al. "Analysis of Protein Structure for Drug Repurposing Using Computational Intelligence and ML Algorithm." IJSSCI vol.14, no.1 2022: pp.1-11. http://doi.org/10.4018/IJSSCI.312562

APA

Srivastava, D., Chui, K. T., Arya, V., Peñalvo, F. J., Kumar, P., & Singh, A. K. (2022). Analysis of Protein Structure for Drug Repurposing Using Computational Intelligence and ML Algorithm. International Journal of Software Science and Computational Intelligence (IJSSCI), 14(1), 1-11. http://doi.org/10.4018/IJSSCI.312562

Chicago

Srivastava, Deepak, et al. "Analysis of Protein Structure for Drug Repurposing Using Computational Intelligence and ML Algorithm," International Journal of Software Science and Computational Intelligence (IJSSCI) 14, no.1: 1-11. http://doi.org/10.4018/IJSSCI.312562

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Abstract

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.

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