DeepTFBS: Transkripsiyon Faktörü Bağlanma Bölgeleri Tahmini İçin Derin Öğrenme Yöntemleri Kullanan Hibrit Bir Model

Year 2024, EARLY VIEW, 1 - 1

Ayşegül Hatipoğlu , Volkan Altuntaş

https://doi.org/10.2339/politeknik.1509329

Abstract

Moleküler seviyede genetik verinin oluşum, aktarım ve düzenlenme süreçleri anlaşılması zor karmaşık kombinasyonel süreçlerden oluşmaktadır. Bu süreçlerin temelini oluşturan transkripsiyon faktörleri genetik bilginin DNA'dan RNA'ya kopyalanmasını sağlayarak hücrelerin özellik ve fonksiyonlarını belirlemede kritik rol oynar. Özellikle sinir sistemi gibi karmaşık yapıları kontrol eden transkripsiyon faktörleri, gen ifadesini düzenleyerek hastalık, sağlık gibi durumların belirlenmesinde hayati rol oynarlar. Proteinlerin DNA üzerinde bağlandıkları bölgeler, gen ifadelerinin kritik noktalarını belirler ve hücrelerin çeşitli koşullara uyum sağlamasına katkıda bulunur. Genetik hastalıkların teşhis edilmesi ve tedavi edilmesi süreçleri için önemli bir adım olan transkripsiyon faktörü bağlanma bölgelerinin tahmini amacıyla literatürde çeşitli yöntemler geliştirilmiştir. DNA’nın dizi ve şekil özelliklerinin beraber kullanımıyla başarılı sonuçlar elde edilen çeşitli çalışmalar geliştirilmiştir. Bu çalışmada DNA dizileri ve şekillerine dayalı olarak transkripsiyon faktörü etkileşimlerini belirlemek için farklı derin öğrenme teknolojileri birleştirilerek hibrit bir yöntem önerilmiştir. Çalışmada 165 doğrulanmış CHIP-Seq veri kümesi kullanılmıştır.

Keywords

Derin Öğrenme, Transkripsiyon Faktörü, Transkripsiyon Faktörü Bağlanma Bölgeleri

DeepTFBS: A Hybrid Model Using Deep Learning Methods for Transcription Factor Binding Sites Prediction

Abstract

The formation, transmission and regulation of genetic data at the molecular level are complex combinatorial processes that are difficult to understand. Transcription factors, which form the basis of these processes, play a critical role in determining the properties and functions of cells by copying genetic information from DNA to RNA. Transcription factors, which control complex structures such as the nervous system, play a vital role in determining conditions such as disease and health by regulating gene expression. The binding sites of proteins on DNA determine the critical points of gene expression and contribute to the adaptation of cells to various conditions. Various methods have been developed in the literature for the prediction of transcription factor binding sites, which is an important step for the diagnosis and treatment of genetic diseases. Several studies have been developed with successful results obtained by using DNA sequence and shape features together. In this study, a hybrid method is proposed by combining different deep learning technologies to identify transcription factor interactions based on DNA sequences and shapes. 165 validated CHIP-Seq datasets were used in the study.

Keywords

Deep Learning, Transcription Factor, Transcription Factor Binding Sites Prediction

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