J. Biosci. Agric. Res. | Volume 26, Issue 02, 2177-2184 | https://doi.org/10.18801/jbar.260220.266
Article type: Research article | Received: 22.10.2020; Revised: 17.11.2020; First published online: 10 December 2020.
Article type: Research article | Received: 22.10.2020; Revised: 17.11.2020; First published online: 10 December 2020.
Modified naive Bayes classifier for classification of protein-protein interaction sites
Mohammad Ahsan Uddin1 and Md. Shakil Ahmed2
1Department of Statistics, University of Dhaka, Dhaka-1000, Bangladesh.
2Department of Statistics, University of Rajshahi, Rajshahi-6205, Bangladesh.
✉ Corresponding author: [email protected] (Uddin, M.A.).
1Department of Statistics, University of Dhaka, Dhaka-1000, Bangladesh.
2Department of Statistics, University of Rajshahi, Rajshahi-6205, Bangladesh.
✉ Corresponding author: [email protected] (Uddin, M.A.).
Abstract
The prediction of protein-protein interaction sites (PPIs) is a vital importance in biology for understanding the physical and functional interactions between molecules in living systems. There are several classification approaches for the prediction of PPI sites; the naïve Bayes classifier is one of the most popular candidates. But the ordinary naïve Bayes classifier is sensitive to unusual protein sequence profiling feature dataset and sometimes it gives ambiguous prediction results. To overcome this problem we have been modified the naïve Bayes classifier by radial basis function (RBF) kernel for the prediction of PPI sites. We investigate the performance of our proposed method compared with the popular classifiers like linear discriminant analysis (LDA), naïve Bayes classifier (NBC), support vector machine (SVM), AdaBoost and k-nearest neighbor (KNN) by the protein sequence profiling data analysis. The mNBC method showed sensitivity (86%), specificity (81%), accuracy (83%) and MCC (65%) for prediction of PPI sites.
Key Words: Protein Sequences Profiling, PPI sites, Relative Solvent Accessibility (rSA), RBF Kernel and Naïve Bayes Classifier
The prediction of protein-protein interaction sites (PPIs) is a vital importance in biology for understanding the physical and functional interactions between molecules in living systems. There are several classification approaches for the prediction of PPI sites; the naïve Bayes classifier is one of the most popular candidates. But the ordinary naïve Bayes classifier is sensitive to unusual protein sequence profiling feature dataset and sometimes it gives ambiguous prediction results. To overcome this problem we have been modified the naïve Bayes classifier by radial basis function (RBF) kernel for the prediction of PPI sites. We investigate the performance of our proposed method compared with the popular classifiers like linear discriminant analysis (LDA), naïve Bayes classifier (NBC), support vector machine (SVM), AdaBoost and k-nearest neighbor (KNN) by the protein sequence profiling data analysis. The mNBC method showed sensitivity (86%), specificity (81%), accuracy (83%) and MCC (65%) for prediction of PPI sites.
Key Words: Protein Sequences Profiling, PPI sites, Relative Solvent Accessibility (rSA), RBF Kernel and Naïve Bayes Classifier
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MLA
Uddin and Ahmed. “Modified naive Bayes classifier for classification of protein-protein interaction sites”. Journal of Bioscience and Agriculture Research, 26(02), (2020): 2177-2184.
APA
Uddin, M. A. and Ahmed, M. S. (2020). Modified naive Bayes classifier for classification of protein-protein interaction sites. Journal of Bioscience and Agriculture Research, 26(02), 2177-2184.
Chicago
Uddin, M. A. and Ahmed, M. S. “Modified naive Bayes classifier for classification of protein-protein interaction sites”. Journal of Bioscience and Agriculture Research, 26(02), (2020): 2177-2184.
Harvard
Uddin, M. A. and Ahmed, M. S. 2020. Modified naive Bayes classifier for classification of protein-protein interaction sites. Journal of Bioscience and Agriculture Research, 26(02), pp. 2177-2184.
Vancouver
Uddin, MA and Ahmed, MS. Modified naive Bayes classifier for classification of protein-protein interaction sites. Journal of Bioscience and Agriculture Research, 2020 December 26(02): 2177-2184.
Uddin and Ahmed. “Modified naive Bayes classifier for classification of protein-protein interaction sites”. Journal of Bioscience and Agriculture Research, 26(02), (2020): 2177-2184.
APA
Uddin, M. A. and Ahmed, M. S. (2020). Modified naive Bayes classifier for classification of protein-protein interaction sites. Journal of Bioscience and Agriculture Research, 26(02), 2177-2184.
Chicago
Uddin, M. A. and Ahmed, M. S. “Modified naive Bayes classifier for classification of protein-protein interaction sites”. Journal of Bioscience and Agriculture Research, 26(02), (2020): 2177-2184.
Harvard
Uddin, M. A. and Ahmed, M. S. 2020. Modified naive Bayes classifier for classification of protein-protein interaction sites. Journal of Bioscience and Agriculture Research, 26(02), pp. 2177-2184.
Vancouver
Uddin, MA and Ahmed, MS. Modified naive Bayes classifier for classification of protein-protein interaction sites. Journal of Bioscience and Agriculture Research, 2020 December 26(02): 2177-2184.
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