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

Yıl 2019, Cilt: 23 Sayı: 1, 126 - 132, 01.04.2019

Kıvanç Güçkıran İsmail Cantürk Lale Özyılmaz

https://doi.org/10.19113/sdufenbed.453462

Cited By: 17

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

DNA Microarray, Gene Expression, Support Vector Machines, Multilayer Perceptron, LASSO, Relief

LASSO ve Relief Özellik Seçimi Yöntemleri ile DVM, ÇKA ve RO Ağ Yapıları Kullanılarak DNA Mikroçip Gen İfadesi Verisetlerinin Sınıflandırılması

Öz

 DNA Mikroçip teknolojisi, çok sayıda gen ifadesinin aynı anda gözlemlenebilmesini sağlayan özgün bir yöntemdir. Günümüzde bu gen ifadeleri bir çok hastalığı teşhis etmek için kullanılmaktadırlar. Bu çalışma iki özellik seçimi ve ağ yapısını çaprazlayarak birden çok verisetinde karşılaştırma yapmaktadır. Mikroçip verisetlerinde her bir örneğin gen sayısı çok sayıda olduğu için, bilgi kazancı en yüksek olan gen seçimi yapılmalıdır. Bu seçim için Relief ve LASSO özellik seçimi yöntemlerini kullandık. En önemli genler örnekten seçildikten sonra Destek Vektör Makinası (DVM), Çok Katmanlı Algılayıcı (ÇKA) ve Rastgele Orman (RO) gibi sıklıkla kullanılan sınıflandırıcılar kullanılarak veri sınıflandırıldı. LASSO özellik seçimi ve DVM daha önceki çalışmaları doğruluk ve eğitim hızı bakımından geride bırakmaktadır.

Anahtar Kelimeler

DNA Mikroçip, Destek Vektör Makinaları, Çok Katmanlı Algılayıcı, LASSO, Relief, Gen ifadesi

Kaynakça

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