2024-07-24-schwarz24a.md

title	abstract	year	volume	publisher	series	software	layout	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	genre	issued	pdf	extras
DDoS: A Graph neural Network Based Drug Synergy Prediction Algorithm	Drug synergy arises when the combined impact of two drugs exceeds the sum of their individual effects. While single-drug effects on cell lines are well-documented, the scarcity of data on drug synergy, considering the vast array of potential drug combinations, prompts a growing interest in computational approaches for predicting synergies in untested drug pairs. We introduce a Graph Neural Network (\textit{GNN}) based model for drug synergy prediction, which utilizes drug chemical structures and cell line gene expression data. We extract data from the largest available drug combination database (DrugComb) and generate multiple synergy scores (commonly used in the literature) to create seven datasets that serve as a reliable benchmark with high confidence. In contrast to conventional models relying on pre-computed chemical features, our GNN-based approach learns task-specific drug representations directly from the graph structure of the drugs, providing superior performance in predicting drug synergies. Our work suggests that learning task-specific drug representations and leveraging a diverse dataset is a promising approach to advancing our understanding of drug-drug interaction and synergy.	2024	248	PMLR	Proceedings of Machine Learning Research	https://github.com/uzh-dqbm-cmi/graphnn	inproceedings	2640-3498	schwarz24a	0	DDoS: A Graph neural Network Based Drug Synergy Prediction Algorithm	24	38	24-38	24	false	Schwarz, Kyriakos and Mendieta Alicia, Pliego and Mollaysa, Amina and Lara, Planas-Paz and Pauli, Chantal and Allam, Ahmed and Krauthammer, Michael	given family Kyriakos Schwarz given family Pliego Mendieta Alicia given family Amina Mollaysa given family Planas-Paz Lara given family Chantal Pauli given family Ahmed Allam given family Michael Krauthammer	2024-07-24		Proceedings of the fifth Conference on Health, Inference, and Learning	inproceedings	date-parts 2024 7 24	https://raw.githubusercontent.com/mlresearch/v248/main/assets/schwarz24a/schwarz24a.pdf