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.
