Title

Prediction of Protein Acetylation Sites using Kernel Naive Bayes Classifier Based on Protein Sequences Profiling

Authors

Md. Shakil Ahmed¹*, Md. Shahjaman², Enamul Kabir³, Md. Kamruzzaman⁴

Affiliation

¹Department of Statistics, University of Rajshahi, Rajshahi-6205, Bangladesh;

²Department of Statistics, Begum Rokeya University, Rangpur-5400, Bangladesh;

³School of Agricultural, Computational and Environmental Sciences, University of Southern Queensland, Australia;

⁴Data Science for Knowledge Creation Research Center, Seoul National University, Korea;

Email

shakil.statru@gmail.com;

Article Type

Hypothesis

Date

Received March 17, 2018; Revised April 29, 2018; Accepted April 29, 2018; Published May 31, 2018

Lysine acetylation is one of the decisive categories of protein post-translational modification (PTM), it is convoluted in many significant cellular developments and severe diseases in the biological system. The experimental identification of protein-acetylated sites is painstaking, time-consuming and expensive. Hence, there is significant interest in the development of computational approaches for consistent prediction of acetylation sites using protein sequences. Features selection from protein sequences plays a significant role for acetylation sites prediction. We describe an improved feature selection approach for acetylation sites prediction based on kernel naïve Bayes classifier (KNBC). We have shown that KNBC generated from selected features by a new feature selection method outperforms than the existing methods for identification of acetylation sites. The sensitivity, specificity, ACC (Accuracy), MCC (Matthews Correlation Coefficient) and AUC (Area under Curve of ROC) in our proposed method are as follows 80.71%, 93.39%, 76.73%, 41.37% and 83.0% with the optimum window size is 47. Thus the kernel naïve Bayes classifier finds application in acetylation site prediction.

Keywords

Acetylation, Protein Sequences, Kernel Naive Bayes Classifier, Binary Encoding, CKSAAP Encoding and Kruskal-Wallis test.

Citation

Ahmed et al. Bioinformation 14(5): 213-218 (2018)

Edited by

P Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.