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Current Pharmaceutical Design

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ISSN (Print): 1381-6128
ISSN (Online): 1873-4286

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Research Article

Prediction of K562 Cells Functional Inhibitors Based on Machine Learning Approaches

Author(s): Yuan Zhang, Zhenyan Han, Qian Gao, Xiaoyi Bai, Chi Zhang* and Hongying Hou*

Volume 25, Issue 40, 2019

Page: [4296 - 4302] Pages: 7

DOI: 10.2174/1381612825666191107092214

Price: $65

Abstract

Background: β thalassemia is a common monogenic genetic disease that is very harmful to human health. The disease arises is due to the deletion of or defects in β-globin, which reduces synthesis of the β-globin chain, resulting in a relatively excess number of α-chains. The formation of inclusion bodies deposited on the cell membrane causes a decrease in the ability of red blood cells to deform and a group of hereditary haemolytic diseases caused by massive destruction in the spleen.

Methods: In this work, machine learning algorithms were employed to build a prediction model for inhibitors against K562 based on 117 inhibitors and 190 non-inhibitors.

Results: The overall accuracy (ACC) of a 10-fold cross-validation test and an independent set test using Adaboost were 83.1% and 78.0%, respectively, surpassing Bayes Net, Random Forest, Random Tree, C4.5, SVM, KNN and Bagging.

Conclusion: This study indicated that Adaboost could be applied to build a learning model in the prediction of inhibitors against K526 cells.

Keywords: Machine learning, cross-validation test, independent set test, Adaboost; feature selection, K526 cells.

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