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Research Article

Prediction of K562 Cells Functional Inhibitors Based on Machine Learning Approaches

[ Vol. 25 , Issue. 40 ]

Author(s):

Yuan Zhang, Zhenyan Han, Qian Gao, Xiaoyi Bai, Chi Zhang* and Hongying Hou*   Pages 4296 - 4302 ( 7 )

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.

Affiliation:

Department of Obstetrics, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510630, Department of Obstetrics, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510630, Department of Obstetrics, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510630, Department of Obstetrics, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510630, University of Auckland, Auckland, Department of Obstetrics, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510630



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