Co-ABC : correlation artificial bee colony algorithm for biomarker gene discovery using gene expression profile

الملخص EN

In this paper, we propose a new hybrid method based on Correlation-based feature selection method and Artificial Bee Colony algorithm,namely Co-ABC to select a small number of relevant genes for accurate classification of gene expression profile.

The Co-ABC consists of three stages which are fully cooperated: The first stage aims to filter noisy and redundant genes in high dimensionality domains by applying Correlation-based feature Selection (CFS) filter method.

In the second stage, Artificial Bee Colony (ABC) algorithm is used to select the informative and meaningful genes.

In the third stage, we adopt a Support Vector Machine (SVM) algorithm as classifier using the preselected genes form second stage.

The overall performance of our proposed Co-ABC algorithm was evaluated using six gene expression profile for binary and multi-class cancer datasets.

In addition, in order to proof the efficiency of our proposed Co-ABC algorithm, we compare it with previously known related methods.

Two of these methods was reimplemented for the sake of a fair comparison using the same parameters.

These two methods are: Co- GA, which is CFS combined with a genetic algorithm GA.

The second one named Co-PSO, which is CFS combined with a particle swarm optimization algorithm PSO.

The experimental results shows that the proposed Co-ABC algorithm acquire the accurate classification performance using small number of predictive genes.

This proofs that Co-ABC is a efficient approach for biomarker gene discovery using cancer gene expression profile.