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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Applying Machine Learning Techniques for Classifying Cyclin-Dependent Kinase Inhibitors

Author 1: Ibrahim Z. Abdelbaky
Author 2: Ahmed F. Al-Sadek
Author 3: Amr A. Badr

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2018.091132

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 9 Issue 11, 2018.

  • Abstract and Keywords
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Abstract: The importance of protein kinases made them a target for many drug design studies. They play an essential role in cell cycle development and many other biological processes. Kinases are divided into different subfamilies according to the type and mode of their enzymatic activity. Computational studies targeting kinase inhibitors identification is widely considered for modelling kinase-inhibitor. This modelling is expected to help in solving the selectivity problem arising from the high similarity between kinases and their binding profiles. In this study, we explore the ability of two machine-learning techniques in classifying compounds as inhibitors or non-inhibitors for two members of the cyclin-dependent kinases as a subfamily of protein kinases. Random forest and genetic programming were used to classify CDK5 and CDK2 kinases inhibitors. This classification is based on calculated values of chemical descriptors. In addition, the response of the classifiers to adding prior information about compounds promiscuity was investigated. The results from each classifier for the datasets were analyzed by calculating different accuracy measures and metrics. Confusion matrices, accuracy, ROC curves, AUC values, F1 scores, and Matthews correlation, were obtained for the outputs. The analysis of these accuracy measures showed a better performance for the RF classifier in most of the cases. In addition, the results show that promiscuity information improves the classification accuracy, but its significant effect was notably clear with GP classifiers.

Keywords: CDK inhibitors; random forest classification; genetic programming classification

Ibrahim Z. Abdelbaky, Ahmed F. Al-Sadek and Amr A. Badr, “Applying Machine Learning Techniques for Classifying Cyclin-Dependent Kinase Inhibitors” International Journal of Advanced Computer Science and Applications(IJACSA), 9(11), 2018. http://dx.doi.org/10.14569/IJACSA.2018.091132

@article{Abdelbaky2018,
title = {Applying Machine Learning Techniques for Classifying Cyclin-Dependent Kinase Inhibitors},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.091132},
url = {http://dx.doi.org/10.14569/IJACSA.2018.091132},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {11},
author = {Ibrahim Z. Abdelbaky and Ahmed F. Al-Sadek and Amr A. Badr}
}


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