Metrik Öğrenmesi Kullanarak Çeşitli Kanser Dokularına Ait Mikro Dizi Gen Verilerinin Sınıflandırılması

Abstract

Kanserli dokuların heterojen doğası gereği birçok kanserin alt türü vardır, ve bu alt türler tespit edilmedikçe kanser tedavisi hedefi bulamaz. Mikrodizi gen teknolojisi ve veri teknolojisinin gelişmesiyle beraber, son yıllarda kanserli dokulara ait mikro dizi gen ifadesi verilerini kullanarak makine öğrenmesi yardımıyla kanserlerin alt türünü tespit etmek yaygınlaşmıştır. Fakat burada asıl problem, veri setinde her bir gene bir özniteliğin karşılık gelmesi, bu yüzden yüksek boyut probleminin ortaya çıkmasıdır. Bu çalışmada üç farklı metrik öğrenmesi metodu (LMNN, ITML ve NCA) ayrı ayrı kullanılarak çeşitli kanser türlerine ait mikro dizi gen veri setleri boyutu azaltılmış uzaylara transfer edilmiştir. Bu sayede, PCA gibi klasik boyut azaltma yöntemlerinden farklı olarak boyutu azaltılmış uzayda, aynı sınıfa (kanser alt türüne) ait örnekleri birbirine yaklaştırılırken, farklı sınıflara ait örnekleri birbirinden uzaklaştırılmıştır. t-SNE metodu yardımıyla azaltılmış boyutlu uzaylar görüntülenerek sınıfların birbirinden ayrıştığı teyit edilmiştir. İlaveten, bu yeni uzaylarda sınıflama algoritmalarının daha performanslı çalıştığını göstermek amacıyla, k-NN, en yakın merkez ve LVQ gibi örnek temelli (instance-based) sınıflama algoritmaları çalıştırılmış ve bu algoritmaların kanser türlerini tespit etmede orjinal uzaydaki performanslarına göre yaklaşık %30'a kadar performanslarının arttığı gözlemlenmiştir.

Keywords

• Mikroarray Gen Verisi
• Kanser Sınıflandırma
• Metrik Öğrenmesi
• Örnek Temelli Sınıflama

Classifying Microarray Gene Data of Various Cancerous Tissues Using Metric Learning

Abstract

Due to the heterogeneous structure of cancerous tissues, they have several subclasses. Unless the subclasses are detected, the cancer treatment cannot be carried out accurately. With the advent of microarray gene technology and data science technology, employing machine learning methods that use the microarray gene expression data of the cancerous tissues for classifying the cancer subclasses has gained an increasing popularity. However, as there exists one feature for each gene, the issue of the curse of dimensionality arises. In the present study, the microarray gene expression data of various cancer types were transferred to some dimensionality reduced spaces by the means of three metric learning methods: LMNN, ITML and NCA. As a result, the instances of the same classes come closer in the reduced space; while those from different classes locate far from each other, which is different from the conventional dimensionality reduction methods, such as PCA, do. To verify this, dimensionality reduced spaces created by the t-SNE method were monitored. Additionally, to show that the classification algorithms perform better in such new spaces, instance-based classifiers, e.g. k-NN, the nearest mean classifier and the LVQ, were built and then it was observed that the performances of the classifiers increased up to 30% in comparison with their performances in the original space.  

Keywords

• Cancer Classification
• Metric Learning
• Microarray Gene Expressions
• Instance-based Classification

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