Research Article

DNA Microarray Gene Expression Data Classification Using SVM, MLP, and RF with Feature Selection Methods Relief and LASSO

Volume: 23 Number: 1 April 1, 2019
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DNA Microarray Gene Expression Data Classification Using SVM, MLP, and RF with Feature Selection Methods Relief and LASSO

Abstract

DNA microarray technology is a novel method to monitor expression levels of large number of genes simultaneously. These gene expressions can be and is being used to detect various forms of diseases. Using multiple microarray datasets, this paper cross compares two different methods for classification and feature selection. Since individual gene count in microarray datas are too many, most informative genes should be selected and used. For this selection, we have tried Relief and LASSO feature selection methods. After selecting informative genes from microarray data, classification is performed with Support Vector Machines (SVM) and Multilayer Perceptron Networks (MLP) which both are widely used in multiple classification tasks. The overall accuracy with LASSO and SVM outperforms most of the approaches proposed.

Keywords

References

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Details

Primary Language

English

Subjects

Engineering

Journal Section

Research Article

Publication Date

April 1, 2019

Submission Date

August 14, 2018

Acceptance Date

April 2, 2019

Published in Issue

Year 2019 Volume: 23 Number: 1

APA
Güçkıran, K., Cantürk, İ., & Özyılmaz, L. (2019). DNA Microarray Gene Expression Data Classification Using SVM, MLP, and RF with Feature Selection Methods Relief and LASSO. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 23(1), 126-132. https://doi.org/10.19113/sdufenbed.453462
AMA
1.Güçkıran K, Cantürk İ, Özyılmaz L. DNA Microarray Gene Expression Data Classification Using SVM, MLP, and RF with Feature Selection Methods Relief and LASSO. J. Nat. Appl. Sci. 2019;23(1):126-132. doi:10.19113/sdufenbed.453462
Chicago
Güçkıran, Kıvanç, İsmail Cantürk, and Lale Özyılmaz. 2019. “DNA Microarray Gene Expression Data Classification Using SVM, MLP, and RF With Feature Selection Methods Relief and LASSO”. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi 23 (1): 126-32. https://doi.org/10.19113/sdufenbed.453462.
EndNote
Güçkıran K, Cantürk İ, Özyılmaz L (April 1, 2019) DNA Microarray Gene Expression Data Classification Using SVM, MLP, and RF with Feature Selection Methods Relief and LASSO. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi 23 1 126–132.
IEEE
[1]K. Güçkıran, İ. Cantürk, and L. Özyılmaz, “DNA Microarray Gene Expression Data Classification Using SVM, MLP, and RF with Feature Selection Methods Relief and LASSO”, J. Nat. Appl. Sci., vol. 23, no. 1, pp. 126–132, Apr. 2019, doi: 10.19113/sdufenbed.453462.
ISNAD
Güçkıran, Kıvanç - Cantürk, İsmail - Özyılmaz, Lale. “DNA Microarray Gene Expression Data Classification Using SVM, MLP, and RF With Feature Selection Methods Relief and LASSO”. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi 23/1 (April 1, 2019): 126-132. https://doi.org/10.19113/sdufenbed.453462.
JAMA
1.Güçkıran K, Cantürk İ, Özyılmaz L. DNA Microarray Gene Expression Data Classification Using SVM, MLP, and RF with Feature Selection Methods Relief and LASSO. J. Nat. Appl. Sci. 2019;23:126–132.
MLA
Güçkıran, Kıvanç, et al. “DNA Microarray Gene Expression Data Classification Using SVM, MLP, and RF With Feature Selection Methods Relief and LASSO”. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, vol. 23, no. 1, Apr. 2019, pp. 126-32, doi:10.19113/sdufenbed.453462.
Vancouver
1.Kıvanç Güçkıran, İsmail Cantürk, Lale Özyılmaz. DNA Microarray Gene Expression Data Classification Using SVM, MLP, and RF with Feature Selection Methods Relief and LASSO. J. Nat. Appl. Sci. 2019 Apr. 1;23(1):126-32. doi:10.19113/sdufenbed.453462

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