Gene expression classification using binary rule majority voting genetic programming classifier

Abstract

The results of a gene expression study are difficult to interpret. To increase interpretability, researchers have developed classification techniques that produce rules to classify gene expression profiles. Genetic programming is one method to produce classification rules. These rules are difficult to interpret because they are based on complicated functions of gene expression values. We propose the binary rule majority voting genetic programming classifier BRMVGPC that classifies samples using binary rules based on the detection calls for genes instead of the gene expression values. BRMVGPC increases rule interpretability. We evaluate BRMVGPC on two public datasets, one brain and one prostate cancer, and achieved 88.89% and 86.39% accuracy respectively. These results are comparable to other classifiers in the gene expression profile domain. Specific contributions include a classification technique BRMVGPC and an iterative k-nearest neighbour technique for handling marginal detection call values.