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

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.

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“…Gene Assortment for the Rebuilding of Stem Cell Differentiation Trees: A Linear Programming Approach 14 , using genetic factor appearance data at both node, we construct a prejudiced Euclidean distance metric such that the smallest spanning tree with admiration to that metric is exactly the given difference hierarchy. We deliver a set of linear restraints that are provably adequate for the wanted building and a linear programming method to classify sparse sets of weights, efficiently identifying genetic factor that is greatest relevant for discerning different shares of the tree.…”

Artificial neural network classification of microarray data using new hybrid gene selection method

“…The ABC algorithm that is innovated in 2005 by Karaboga (2005) is one of the bioinspired evolutionary techniques, which has been employed to identify an optimal solution in different optimisation problems that based on the metaphor of the bees foraging behaviour (Alshamlan et al, 2015). It is as simple as PSO and Differential Evolution (DE) algorithms, and uses only common control parameters such as colony size and maximum cycle number.…”

“…On the other hand, bio-inspired and evolutionary algorithms have been widely applied for genes selection that classify the disease with best accuracy (El Akadi et al, 2009;Alshamlan et al, 2015;Alshamlan et al, 2014;Yu et al, 2013b). A non-parallel plane proximal classifier is described in Ghorai et al (2011), where the authors used genetic algorithms for selecting genes for a cancer classification and the results are compared against an SVM.…”

Survey on different Methods for Classifying Gene Expression using Microarray Approach

“…Hala Alshamlan, et al [31] tried to create a profile for microarray gene expression. Researchers applied feature selection phase using an innovation of a hybrid system between swarm intelligence algorithm called (ABC) artificial bee colony and a method called (mRMR) minimum redundancy maximum relevance in order to select an informative features (genes) for better prediction for classification.…”