DRGAT: Predicting drug responses via diffusion-based graph attention network

Name: DRGAT: Predicting drug responses via diffusion-based graph attention network
Author: Sefer, Emre

İsim	DRGAT: Predicting drug responses via diffusion-based graph attention network
Yazar	Sefer, Emre
Basım Tarihi:	2025-03-01
Basım Yeri	- Mary Ann Liebert
Konu	Graph neural network, Drug response prediction, Drug discovery, Diffusion, Deep learning
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1066-5277
Kayıt Numarası	cf1d1973-06f6-40b4-b8bf-f840e74f2ed1
Lokasyon	Computer Science
Tarih	2025-03-01
Notlar	TÜBİTAK
Örnek Metin	Accurately predicting drug response depending on a patient's genomic profile is critical for advancing personalized medicine. Deep learning approaches rise and especially the rise of graph neural networks leveraging large-scale omics datasets have been a key driver of research in this area. However, these biological datasets, which are typically high dimensional but have small sample sizes, present challenges such as overfitting and poor generalization in predictive models. As a complicating matter, gene expression (GE) data must capture complex inter-gene relationships, exacerbating these issues. In this article, we tackle these challenges by introducing a drug response prediction method, called drug response graph attention network (DRGAT), which combines a denoising diffusion implicit model for data augmentation with a recently introduced graph attention network (GAT) with high-order neighbor propagation (HO-GATs) prediction module. Our proposed approach achieved almost 5% improvement in the area under receiver operating characteristic curve compared with state-of-the-art models for the many studied drugs, indicating our method's reasonable generalization capabilities. Moreover, our experiments confirm the potential of diffusion-based generative models, a core component of our method, to mitigate the inherent limitations of omics datasets by effectively augmenting GE data.
DOI	10.1089/cmb.2024.0807
Cilt	32

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DRGAT: Predicting drug responses via diffusion-based graph attention network

Yazar Sefer, Emre

Basım Tarihi 2025-03-01

Basım Yeri - Mary Ann Liebert

Konu Graph neural network, Drug response prediction, Drug discovery, Diffusion, Deep learning

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1066-5277

Kayıt Numarası cf1d1973-06f6-40b4-b8bf-f840e74f2ed1

Lokasyon Computer Science

Tarih 2025-03-01

Notlar TÜBİTAK

Örnek Metin Accurately predicting drug response depending on a patient's genomic profile is critical for advancing personalized medicine. Deep learning approaches rise and especially the rise of graph neural networks leveraging large-scale omics datasets have been a key driver of research in this area. However, these biological datasets, which are typically high dimensional but have small sample sizes, present challenges such as overfitting and poor generalization in predictive models. As a complicating matter, gene expression (GE) data must capture complex inter-gene relationships, exacerbating these issues. In this article, we tackle these challenges by introducing a drug response prediction method, called drug response graph attention network (DRGAT), which combines a denoising diffusion implicit model for data augmentation with a recently introduced graph attention network (GAT) with high-order neighbor propagation (HO-GATs) prediction module. Our proposed approach achieved almost 5% improvement in the area under receiver operating characteristic curve compared with state-of-the-art models for the many studied drugs, indicating our method's reasonable generalization capabilities. Moreover, our experiments confirm the potential of diffusion-based generative models, a core component of our method, to mitigate the inherent limitations of omics datasets by effectively augmenting GE data.

DOI 10.1089/cmb.2024.0807

Cilt 32