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Comparative Study
. 2016 Aug 10;9 Suppl 2(Suppl 2):47.
doi: 10.1186/s12920-016-0204-7.

Gene selection for cancer classification with the help of bees

Johra Muhammad Moosa  1 , Rameen Shakur  2 , Mohammad Kaykobad  3 , Mohammad Sohel Rahman  3
Affiliations
Comparative Study

Gene selection for cancer classification with the help of bees

Johra Muhammad Moosa et al. BMC Med Genomics. .

Abstract

Background: Development of biologically relevant models from gene expression data notably, microarray data has become a topic of great interest in the field of bioinformatics and clinical genetics and oncology. Only a small number of gene expression data compared to the total number of genes explored possess a significant 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 classification, it can also cut down the time and cost of medical diagnoses.

Methods: This study presents a modified Artificial Bee Colony Algorithm (ABC) to select minimum number of genes that are deemed to be significant for cancer along with improvement of predictive accuracy. The search equation of ABC is believed to be good at exploration but poor at exploitation. To overcome this limitation we have modified the ABC algorithm by incorporating the concept of pheromones which is one of the major components of Ant Colony Optimization (ACO) algorithm and a new operation in which successive bees communicate to share their findings.

Results: The proposed algorithm is evaluated using a suite of ten publicly available datasets after the parameters are tuned scientifically with one of the datasets. Obtained results are compared to other works that used the same datasets. The performance of the proposed method is proved to be superior.

Conclusion: The method presented in this paper can provide subset of genes leading to more accurate classification results while the number of selected genes is smaller. Additionally, the proposed modified Artificial Bee Colony Algorithm could conceivably be applied to problems in other areas as well.

Keywords: Artificial bee colony algorithm; Cancer classification; Evolutionary algorithm; Gene selection; Microarray.

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Figures

Fig. 1
Fig. 1
The flowchart of the modified Artificial Bee Colony (mABC) algorithm
Fig. 2
Fig. 2
Distribution of classification accuracy using first (optimized) parameter setting for the dataset a 9_Tumors; b 11_Tumors
Fig. 3
Fig. 3
Distribution of number of times selected gene size fall in a specific range using the first (optimized) parameter setting a 9_Tumors; b 11_Tumors; c Brain_Tumor1; d Brain_Tumor2e Leukemia1; f Leukemia2; gDLBCL; h Lung_Cancer; i Prostate_Tumor; jSRBCT
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