Identifying ion channel genes related to cardiomyopathy using a novel decision forest strategy

Abstract

Ion channels play many crucial functions in life. Their dysfunction may lead to a number of diseases, such as arrhythmia and beta cell dysfunction. In this study, we firstly selected the ion channel gene expression profiles using a dimensionality reduction method. After that, we applied a novel decision forest strategy to mine cardiomyopathy related ion channel genes. The novel proposed Z_i integrated the information of the decision trees' height and the frequency at which a gene was located in the tree. It achieved a much higher ability of feature selection. In the result, 26 cardiomyopathy related ion channel genes were identified. Their Z_i were higher than the threshold Z*. Furthermore, most of these genes had been reported to have relationships with cardiomyopathies. In conclusion, our proposed decision forest strategy had a better classification performance. Our result can provide a theoretical basis for cardiovascular researchers.