Araştırma Makalesi

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

Cilt: 23 Sayı: 1 1 Nisan 2019
PDF İndir
EN TR

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

Öz

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.

Anahtar Kelimeler

Kaynakça

  1. [1] Schena, M., Shalon, D., Davis, R. W., & Brown, P. O. (1995). Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science, 270(5235), 467-470.
  2. [2] Alizadeh, Ash & B Eisen, Michael & Davis, Richard & Ma, Chi & S Lossos, Izidore & Rosenwald, Andreas & C Boldrick, Jennifer & Sabet, Hajeer & Tran, Truc & Yu, Xin. (2000). Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature. 403. 503-511.
  3. [3] Hira, Z. M., & Gillies, D. F. (2015). A Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data. Advances in Bioinformatics, 2015, 198363.
  4. [4] Kira, K., & Rendell, L. A. (1992). A practical approach to feature selection. In Machine Learning Proceedings 1992 (pp. 249-256).
  5. [5] Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological), 267-288.
  6. [6] Brown, M. P., Grundy, W. N., Lin, D., Cristianini, N., Sugnet, C., Ares, M., & Haussler, D. (1999). Support vector machine classification of microarray gene expression data. University of California, Santa Cruz, Technical Report UCSC-CRL-99-09.
  7. [7] Rafii, F., Kbir, M. H. A., & Hassani, B. D. R. (2015, November). MLP network for lung cancer presence prediction based on microarray data. In Complex Systems (WCCS), 2015 Third World Conference on (pp. 1-6). IEEE.
  8. [8] Díaz-Uriarte, R., & De Andres, S. A. (2006). Gene selection and classification of microarray data using random forest. BMC Bioinformatics, 7(1), 3.

Ayrıntılar

Birincil Dil

İngilizce

Konular

Mühendislik

Bölüm

Araştırma Makalesi

Yayımlanma Tarihi

1 Nisan 2019

Gönderilme Tarihi

14 Ağustos 2018

Kabul Tarihi

2 Nisan 2019

Yayımlandığı Sayı

Yıl 2019 Cilt: 23 Sayı: 1

Kaynak Göster

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. Süleyman Demirel Üniv. Fen Bilim. Enst. Derg. 2019;23(1):126-132. doi:10.19113/sdufenbed.453462
Chicago
Güçkıran, Kıvanç, İsmail Cantürk, ve 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 (01 Nisan 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, ve L. Özyılmaz, “DNA Microarray Gene Expression Data Classification Using SVM, MLP, and RF with Feature Selection Methods Relief and LASSO”, Süleyman Demirel Üniv. Fen Bilim. Enst. Derg., c. 23, sy 1, ss. 126–132, Nis. 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 (01 Nisan 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. Süleyman Demirel Üniv. Fen Bilim. Enst. Derg. 2019;23:126–132.
MLA
Güçkıran, Kıvanç, vd. “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, c. 23, sy 1, Nisan 2019, ss. 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. Süleyman Demirel Üniv. Fen Bilim. Enst. Derg. 01 Nisan 2019;23(1):126-32. doi:10.19113/sdufenbed.453462

Cited By

e-ISSN :1308-6529
Linking ISSN (ISSN-L): 1300-7688

Dergide yayımlanan tüm makalelere ücretiz olarak erişilebilinir ve Creative Commons CC BY-NC Atıf-GayriTicari lisansı ile açık erişime sunulur. Tüm yazarlar ve diğer dergi kullanıcıları bu durumu kabul etmiş sayılırlar. CC BY-NC lisansı hakkında detaylı bilgiye erişmek için tıklayınız.