Selecting significant marker genes from microarray data by filter approach for cancer diagnosis

Bouazza, Sara Haddou; Auhmani, Khalid; Zeroual, Abdelouhab; Hamdi, Nezha

doi:10.1016/j.procs.2018.01.126

Cited by 16 publications

(16 citation statements)

References 11 publications

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“…Even though Alshamlan [5] and Motieghader et al [16] obtained slightly higher accuracy for Colon cancer classification, yet they are of larger gene's subset. Bouazza et al [31] suggested a filter approach which produced 30 genes for Ovarian cancer classification whereas we could obtain the same accuracy while using only three genes. The same outcome can be seen in the work proposed by Gunavathi & Premalatha [9] who proposed GA for gene selection.…”

Section: Resultsmentioning

confidence: 72%

Incremental Search for Informative Gene Selection in Cancer Classification

Fajila¹,

Yusof²

2021

AETiC

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Although numerous methods of using microarray data analysis for classification have been reported, there is space in the field of cancer classification for new inventions in terms of informative gene selection. This study introduces a new incremental search-based gene selection approach for cancer classification. The strength of wrappers in determining relevant genes in a gene pool can be increased as they evaluate each possible gene’s subset. Nevertheless, the searching algorithms play a major role in gene’s subset selection. Hence, there is the possibility of finding more informative genes with incremental application. Thus, we introduce an approach which utilizes two searching algorithms in gene’s subset selection. The approach was efficient enough to classify five out of six microarray datasets with 100% accuracy using only a few biomarkers while the rest classified with only one misclassification.

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Section: Resultsmentioning

confidence: 72%

Incremental Search for Informative Gene Selection in Cancer Classification

Fajila¹,

Yusof²

2021

AETiC

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“…With the improvement of current high the productivity of biotechnology, advantage selection rapidly includes its performance in the analysis of the vast quantity of producing data 18 . The selection of genes in microarray data is one of these important applications because microarray data sets usually have a high advantage ratio for the sample, that is, several thousand features (genes) with only a few dozen samples 19 . There are many features selection methods applied in our approaches such as ReliefF, MultiSURF Lasso, Elastic Net, and Elastic Net CV.…”

Section: Materials and The Methodsmentioning

confidence: 99%

Hybrid feature selection model based on relief‐based algorithms and regulizer algorithms for cancer classification

Manhrawy

Qaraad

Elkafrawy

2021

Concurrency and Computation

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Summary Cancer is a group of diseases that involve abnormal cell growth with the potential to spread to other parts of the body. Cancer microarray data usually include a small number of samples with a large number of gene expression levels as features. Gene expression or microarray is a technology that monitors the expression of the large number of genes in parallel that make it useful in cancer classification, high dimensionality in cancer microarray data results in the overfitting problem. This article proposes novel hybrid feature selection model called the RBARegulizer model, which is based on two types of feature selection techniques, two RBAs algorithms (ReliefF, MultiSURF) for feature‐ranking filters to the most important one's genes, and three regulizer algorithms (Lasso, Elastic Net, Elastic Net CV) to reduce the feature subset, remove the noisy and irrelevant feature to improve the performance and accuracy of cancer (microarray) data classification. For evaluating the model, the different three classifiers SVM, MLP, and random forest with four high‐dimensional microarray data for different cancer types were applied. The experimental type shows that our model overcomes the overfitting problem of cancer microarray data. Moreover, the results show that RBARegulizer model is perfect in improving the accuracy of cancer microarray data classification.

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“…Recently, Bouazza et al [11] carried out a comparative study on feature subset selection. Five cancer microarray datasets were evaluated using five supervised classifiers.…”

Section: Related Workmentioning

confidence: 99%

“…Consequently, they [11] suggested the signal to noise ratio feature selection method with K Nearest Neighbors classifier for feature selection. Comparatively wrappers and filters are used simply with good performance [2,8,[11][12][13][14][15][16][17]. Yet, some studies show lack of performance due to direct application of wrappers into the original datasets [2].…”

Section: Related Workmentioning

confidence: 99%

The Effect of Evolutionary Algorithm in Gene Subset Selection for Cancer Classification

Fajila¹,

Jahan²

2018

IJMECS

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The fact that reflects the cancer research consequences shows that still there are improvements that should be investigated in the stream of cancer in future. This leads the researchers to actively involve further in cancer research field. As an invention, a hybrid machine learning method is proposed in this study where two filters are assessed along with a wrapper approach. Typically, filters prioritize the features while, wrappers contribute in subset identification. Though both filters and wrappers exist independently, the excellent results they produce when applied subsequently. The wrapperfilter combination plays a major role in feature selection. Yet, incorporating with a best strategy for feature space analysis is crucial in this concern. Thus, we introduce the Evolutionary Algorithm in the proposed study to search through the feature space for informative gene subset selection. Though there are several gene selection approaches for cancer classification, many of them suffer from law classification accuracy and huge gene subset for prediction. Hence, we propose Evolutionary Algorithm to overcome this problem. The proposed approach is evaluated on five microarray datasets, where three out of them provide 100% accuracy. Regardless the number of genes selected, both filters provide the same performance throughout the datasets used. As a consequence, the Evolutionary Algorithm in feature space search is highlighted for its performance in gene subset selection.

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Selecting significant marker genes from microarray data by filter approach for cancer diagnosis

Cited by 16 publications

References 11 publications

Incremental Search for Informative Gene Selection in Cancer Classification

Incremental Search for Informative Gene Selection in Cancer Classification

Hybrid feature selection model based on relief‐based algorithms and regulizer algorithms for cancer classification

The Effect of Evolutionary Algorithm in Gene Subset Selection for Cancer Classification

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