Identification of cancerous gene groups from microarray data by employing adaptive genetic and support vector machine technique

Abstract

Nowadays, microarray gene expression data plays a vital role in tumor classification. However, due to the accessibility of a limited number of tissues compared to large number of genes in genomic data, various existing methods have failed to identify a small subset of discriminative genes. To overcome this limitation, in this paper, we developed a new hybrid technique for gene selection, called ensemble multipopulation adaptive genetic algorithm (EMPAGA) that can overlook the irrelevant genes and classify cancer accurately. The proposed hybrid gene selection algorithm comprises of two phase. In the first phase, an ensemble gene selection (EGS) method used to filter the noisy and redundant genes in high‐dimensional datasets by combining multilayer and F‐score approaches. Then, an adaptive genetic algorithm based on multipopulation strategy with support vector machine and naïve Bayes (NB) classifiers as a fitness function is applied for gene selection to select the extremely sensible genes from the reduced datasets. The performance of the proposed method is estimated on 10 microarray datasets of numerous tumor. The comprehensive results and various comparisons disclose that EGS has a remarkable impact on the efficacy of the adaptive genetic algorithm with multipopulation strategy and enhance the capability of the proposed approach in terms of convergence rate and solution quality. The experiments results demonstrate the superiority of the proposed method when compared to other standard wrappers regarding classification accuracy and optimal number of genes.

[Retracted] Stability Evaluation of Rock Slope in Hydraulic Engineering Based on Improved Support Vector Machine Algorithm

“…In the analysis of the slope, if the factors are not considered comprehensively, the results obtained by the model will be far from the actual situation, which means that it is difficult to use accurate and reliable mathematical model to reflect the formation mechanism and mechanical behavior process of the highly nonlinear slope. erefore, genetic algorithm, artificial neural network, simulated annealing algorithm, ant colony algorithm, support vector machine (SVM), and the calculation methods of artificial intelligence to solve the problem of slope engineering of nonlinear problems provide a new calculation method and train of thought; namely, using this approach, this paper analyzes and researches on the application of SVM method in slope engineering [4][5][6].…”

A genetic algorithm for prediction of RNA-seq malaria vector gene expression data classification using SVM kernels

“…Table 3 shows the comparison of this study with other techniques in literature. [29] 89 Mutual information+KNN [30] 95 GA+MLP [31] 89 RF [32] 94 Bayesian [33] 91…”

Gene Correlation Guided Gene Selection for Microarray Data Classification

“…As to wrapper methods, varying classification algorithms are often used as a fitness evaluation to determine the subset of genes and the selected genes can in turn enhance the classification performance [2,[49][50][51][52][53][54][55][56]. In general, wrapper methods can obtain better results than filter methods, but bring more expensive computational cost.…”