Applying Machine Learning Techniques for Classifying Cyclin-Dependent Kinase Inhibitors

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@Article{Abdelbaky:2018:IJACSA,

• author = "Ibrahim Z. Abdelbaky and Ahmed F. Al-Sadek and Amr A. Badr",
• title = "Applying Machine Learning Techniques for Classifying Cyclin-Dependent Kinase Inhibitors",
• journal = "International Journal of Advanced Computer Science and Applications",
• year = "2018",
• number = "11",
• volume = "9",
• pages = "229--235",
• keywords = "genetic algorithms, genetic programming, cdk inhibitors, random forest classification",
• publisher = "The Science and Information (SAI) Organization",
• bibsource = "OAI-PMH server at thesai.org",
• language = "eng",
• oai = "oai:thesai.org:10.14569/IJACSA.2018.091132",
• URL = "http://thesai.org/Downloads/Volume9No11/Paper_32-Applying_Machine_Learning_Techniques.pdf",
• DOI = "doi:10.14569/IJACSA.2018.091132",
• size = "7 pages",
• 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 analysed 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.",

}

Genetic Programming entries for Ibrahim Z Abdelbaky Ahmed F Al-Sadek Amr A Badr

Citations