Maran, P., R, T. (2018). Informative Gene Selection for Cancer Classification with Microarray Data Using a Metaheuristic Framework. Asian Pacific Journal of Cancer Prevention, 19(2), 561-564. doi: 10.22034/APJCP.2018.19.2.561
Pyingkodi Maran; Thangarajan R. "Informative Gene Selection for Cancer Classification with Microarray Data Using a Metaheuristic Framework". Asian Pacific Journal of Cancer Prevention, 19, 2, 2018, 561-564. doi: 10.22034/APJCP.2018.19.2.561
Maran, P., R, T. (2018). 'Informative Gene Selection for Cancer Classification with Microarray Data Using a Metaheuristic Framework', Asian Pacific Journal of Cancer Prevention, 19(2), pp. 561-564. doi: 10.22034/APJCP.2018.19.2.561
Maran, P., R, T. Informative Gene Selection for Cancer Classification with Microarray Data Using a Metaheuristic Framework. Asian Pacific Journal of Cancer Prevention, 2018; 19(2): 561-564. doi: 10.22034/APJCP.2018.19.2.561
Informative Gene Selection for Cancer Classification with Microarray Data Using a Metaheuristic Framework
1Department of Computer Applications, Kongu Engineering College Erode, TamilNadu, India.
2Department of Computer Science and Engineering, Kongu Engineering College Erode, TamilNadu, India.
Receive Date: 03 November 2017,
Revise Date: 16 November 2017,
Accept Date: 09 January 2018
Abstract
Objective: Cancer diagnosis is one of the most vital emerging clinical applications of microarray data. Due to the high dimensionality, gene selection is an important step for improving expression data classification performance. There is therefore a need for effective methods to select informative genes for prediction and diagnosis of cancer. The main objective of this research was to derive a heuristic approach to select highly informative genes. Methods: A metaheuristic approach with a Genetic Algorithm with Levy Flight (GA-LV) was applied for classification of cancer genes in microarrays. The experimental results were analyzed with five major cancer gene expression benchmark datasets. Result: GA-LV proved superior to GA and statistical approaches, with 100% accuracy for the dataset for Leukemia, Lung and Lymphoma. For Prostate and Colon datasets the GA-LV was 99.5% and 99.2% accurate, respectively. Conclusion: The experimental results show that the proposed approach is suitable for effective gene selection with all benchmark datasets, removing irrelevant and redundant genes to improve classification accuracy.
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