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mRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling.

Alshamlan H, Badr G, Alohali Y - Biomed Res Int (2015)

Bottom Line: An artificial bee colony (ABC) is a relatively recent swarm intelligence optimization approach.We reimplemented two of these techniques for the sake of a fair comparison using the same parameters.The experimental results prove that the proposed mRMR-ABC algorithm achieves accurate classification performance using small number of predictive genes when tested using both datasets and compared to previously suggested methods.

View Article: PubMed Central - PubMed

Affiliation: College of Computer and Information Sciences, King Saud University, P.O. Box 22452, Riyadh 11495, Saudi Arabia.

ABSTRACT
An artificial bee colony (ABC) is a relatively recent swarm intelligence optimization approach. In this paper, we propose the first attempt at applying ABC algorithm in analyzing a microarray gene expression profile. In addition, we propose an innovative feature selection algorithm, minimum redundancy maximum relevance (mRMR), and combine it with an ABC algorithm, mRMR-ABC, to select informative genes from microarray profile. The new approach is based on a support vector machine (SVM) algorithm to measure the classification accuracy for selected genes. We evaluate the performance of the proposed mRMR-ABC algorithm by conducting extensive experiments on six binary and multiclass gene expression microarray datasets. Furthermore, we compare our proposed mRMR-ABC algorithm with previously known techniques. We reimplemented two of these techniques for the sake of a fair comparison using the same parameters. These two techniques are mRMR when combined with a genetic algorithm (mRMR-GA) and mRMR when combined with a particle swarm optimization algorithm (mRMR-PSO). The experimental results prove that the proposed mRMR-ABC algorithm achieves accurate classification performance using small number of predictive genes when tested using both datasets and compared to previously suggested methods. This shows that mRMR-ABC is a promising approach for solving gene selection and cancer classification problems.

No MeSH data available.


Related in: MedlinePlus

The classification accuracy performance of the mRMR method with an SVM classifier for all microarray datasets.
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fig8: The classification accuracy performance of the mRMR method with an SVM classifier for all microarray datasets.

Mentions: In this section, we present and analyze the results that are obtained by our algorithm. As a first step, we employed the mRMR method to identify the top relevant genes that give 100% accuracy with an SVM classifier. From Table 4 and Figure 8, we can see that the top 150 genes in the leukemia1 dataset generate 100% classification accuracy while in the colon dataset, we can get 100% accuracy using 350 genes. For the lung dataset, we achieved 100% accuracy using 200 genes and 250 genes to get the same classification accuracy for the SRBCT dataset. In addition, using 150 high relevant genes from the lymphoma dataset and 250 genes from the leukemia2 dataset, we achieved 100% classification accuracy. Then we used these high relevant genes as input in the ABC algorithm to determine the most predictive and informative genes.


mRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling.

Alshamlan H, Badr G, Alohali Y - Biomed Res Int (2015)

The classification accuracy performance of the mRMR method with an SVM classifier for all microarray datasets.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4414228&req=5

fig8: The classification accuracy performance of the mRMR method with an SVM classifier for all microarray datasets.
Mentions: In this section, we present and analyze the results that are obtained by our algorithm. As a first step, we employed the mRMR method to identify the top relevant genes that give 100% accuracy with an SVM classifier. From Table 4 and Figure 8, we can see that the top 150 genes in the leukemia1 dataset generate 100% classification accuracy while in the colon dataset, we can get 100% accuracy using 350 genes. For the lung dataset, we achieved 100% accuracy using 200 genes and 250 genes to get the same classification accuracy for the SRBCT dataset. In addition, using 150 high relevant genes from the lymphoma dataset and 250 genes from the leukemia2 dataset, we achieved 100% classification accuracy. Then we used these high relevant genes as input in the ABC algorithm to determine the most predictive and informative genes.

Bottom Line: An artificial bee colony (ABC) is a relatively recent swarm intelligence optimization approach.We reimplemented two of these techniques for the sake of a fair comparison using the same parameters.The experimental results prove that the proposed mRMR-ABC algorithm achieves accurate classification performance using small number of predictive genes when tested using both datasets and compared to previously suggested methods.

View Article: PubMed Central - PubMed

Affiliation: College of Computer and Information Sciences, King Saud University, P.O. Box 22452, Riyadh 11495, Saudi Arabia.

ABSTRACT
An artificial bee colony (ABC) is a relatively recent swarm intelligence optimization approach. In this paper, we propose the first attempt at applying ABC algorithm in analyzing a microarray gene expression profile. In addition, we propose an innovative feature selection algorithm, minimum redundancy maximum relevance (mRMR), and combine it with an ABC algorithm, mRMR-ABC, to select informative genes from microarray profile. The new approach is based on a support vector machine (SVM) algorithm to measure the classification accuracy for selected genes. We evaluate the performance of the proposed mRMR-ABC algorithm by conducting extensive experiments on six binary and multiclass gene expression microarray datasets. Furthermore, we compare our proposed mRMR-ABC algorithm with previously known techniques. We reimplemented two of these techniques for the sake of a fair comparison using the same parameters. These two techniques are mRMR when combined with a genetic algorithm (mRMR-GA) and mRMR when combined with a particle swarm optimization algorithm (mRMR-PSO). The experimental results prove that the proposed mRMR-ABC algorithm achieves accurate classification performance using small number of predictive genes when tested using both datasets and compared to previously suggested methods. This shows that mRMR-ABC is a promising approach for solving gene selection and cancer classification problems.

No MeSH data available.


Related in: MedlinePlus