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