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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 gene selection phase (ABC algorithm).
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Related In: Results  -  Collection


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fig6: The gene selection phase (ABC algorithm).

Mentions: (ii) Gene Selection Phase (Figure 6). An ABC algorithm is developed as described in Section 2.1 to select the most informative and predictive genes from an mRMR dataset that give the highest classification accuracy with an SVM classifier. Figure 3 illustrates the representation of the food source or the solution space for the proposed mRMR-ABC algorithm. Each solution is represented as a group of genes indices that are selected form the mRMR dataset. In a gene selection problem, each solution (i.e., subset of selected genes) is associated with the fitness value, which is the classification accuracy using an SVM classifier.


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 gene selection phase (ABC algorithm).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig6: The gene selection phase (ABC algorithm).
Mentions: (ii) Gene Selection Phase (Figure 6). An ABC algorithm is developed as described in Section 2.1 to select the most informative and predictive genes from an mRMR dataset that give the highest classification accuracy with an SVM classifier. Figure 3 illustrates the representation of the food source or the solution space for the proposed mRMR-ABC algorithm. Each solution is represented as a group of genes indices that are selected form the mRMR dataset. In a gene selection problem, each solution (i.e., subset of selected genes) is associated with the fitness value, which is the classification accuracy using an SVM classifier.

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