Evrişimsel Sinir Ağlarını Kullanarak Protein Dizilimlerinin Sınıflandırılması

Biyoinformatikteki en büyük zorluklardan biri, protein yapısının ve fonksiyonunun öngörülmesi ve sınıflandırılmasıdır. Çok miktarda Ribonükleik Asit (RNA) verisi geleneksel laboratuvar yolu kullanılarak yönetilemez. Bunun için proteinler yapılarına ve ailelerine göre ayrılmalıdır. Bu nedenle proteinlerin biyolojik ailelerini ve fonksiyonlarını tanımlamak için sınıflandırılması gerekmektedir. Geleneksel makine öğrenme yaklaşımlarında, proteinler sınıflandırılırken çeşitli özellik çıkarım algoritmaları kullanılmaktadır. Elle özellik çıkarımında, seçilen özellikler, başarımı doğrudan etkilemektedir. Bu nedenle, bu çalışmada önerilen yaklaşımda ise protein sekanslarını amino asit bileşimi yöntemi ile sayısallaştırılmıştır. Sayısallaştırılan protein dizilimleri spektrograma dönüştürülmüş ve iki boyutlu Evrişimsel Sinir Ağı (ESA) modelleri (VGG19, ResNet) kullanılarak otomatik özellik çıkarımı gerçekleştirilmiştir. Çıkarılan özellikler Destek Vektör Makineleri (DVM) ve k-En Yakın Komşuluk (k-NN) ile sınıflandırılmıştır. Sonuç olarak, ResNet kullanılarak gerçekleştirilen protein sekanslarının sınıflandırma işleminde %95.03’lük bir doğruluğa ulaşılmıştır.Anahtar kelimeler: Protein sınıflama, biyoinformatik, evrişimsel sinir ağları, makine öğrenmesi.

Classifying Protein Sequences Using Convolutional Neural Network

One of the major challenges in bioinformatics is the classification and identification of protein structure and function. Large amounts of Ribonucleic Acid (RNA) data cannot be managed using traditional laboratory methods. For this, proteins should be separated according to their structure and families. Therefore, proteins need to be classified to define their biological families and functions. In traditional machine learning approaches, various feature extraction algorithms are used to classify proteins. In manual feature extraction, the selected features directly affect performance. Therefore, in the proposed method of this study, protein sequences were digitized by the amino acid composition technique. The digitized protein sequences were converted to spectrograms, and automatic feature extraction was performed using two-dimensional Convolutional Neural Network (CNN) models (VGG19, ResNet). The extracted features were classified with Support Vector Machine (SVM) and k-Nearest Neighbors (k-NN). As a result, the accuracy of 95.03% was achieved in the classification of protein sequences using ResNet.Keywords: Protein classification, bioinformatics, convolutional neural network, machine learning.

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