Modeifed artificial bee colony algorithm for gene selection in classifying cancer

Abstract

Development of cancer diagnostic models by utilizing microarray data has become a topic of great interest in the eld of bioinformatics and medicine. Only a small number of gene expression data compared to the total number of genes explored possess a signi cant correlation with a certain phenotype. Gene selection enables researchers to obtain substantial insight into the genetic nature of the disease and the mechanisms responsible for it. Besides improvement of the performance of cancer classi cation, it can also cut down the time and cost of medical diagnosis. This study presents a modi ed Arti cial Bee Colony Algorithm (ABC) to select a minimum number of genes that are deemed to be signi cant for cancer along with improvement of predictive accuracy. The search equation of ABC is said to be good at exploration but poor at exploitation. To overcome this limitation we have modi- ed the ABC algorithm by incorporating pheromone which is one of the major components of Ant Colony Optimization (ACO) algorithm and introduced a new operation in which successive bees communicate to share their ndings. The proposed algorithm is evaluated using a suite of ten publicly available datasets after the parameters are scienti cally tuned with one of the datasets. The obtained results are compared to other works which used the same datasets. The performance of the proposed method has been proved to be superior. The method presented in this paper can provide a subset of genes leading to more accurate classi cation results while the number of selected genes is smaller. The proposed modi ed ABC Algorithm could conceivably be applied to problems in other areas.