Reference : Graph neural networks for investigating complex diseases: A case study on Parkinson's...

	Document type :	Scientific congresses, symposiums and conference proceedings : Poster
	Discipline(s) :	Life sciences : Biochemistry, biophysics & molecular biology Life sciences : Multidisciplinary, general & others Engineering, computing & technology : Computer science Engineering, computing & technology : Multidisciplinary, general & others
	Focus Areas :	Computational Sciences; Systems Biomedicine
	To cite this reference:	http://hdl.handle.net/10993/56048

Title :	Graph neural networks for investigating complex diseases: A case study on Parkinson's Disease
Language :	English
Author, co-author :	Gómez de Lope, Elisa [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Biomedical Data Science >]
	Viñas Torné, Ramón []
	Liò, Pietro []
	Glaab, Enrico [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Biomedical Data Science >]
Publication date :	25-Jul-2023
Peer reviewed :	Yes
Audience :	International
Event name :	31st Annual Intelligent Systems For Molecular Biology and the 22nd Annual European Conference on Computational Biology
Event date :	from 23-07-2023 to 27-07-2023
Event organizer :	International Society of Computational Biology
Event place (city) :	Lyon
Event country :	France
Keywords :	[en] Graph representation learning ; omics ; Parkinson's Disease ; machine learning ; graphs ; networks
Abstract :	[en] Omics data analysis is a critical component in the study of complex diseases, but the high dimension and heterogeneity of the data often pose challenges that are difficult to address by classical statistical and machine learning methods. Recently, structured data analyses using graph neural networks (GNNs) have emerged as a promising complementary approach, particularly for investigating the relational information between samples. However, it is still unclear which strategies for designing and optimizing GNNs are most effective when working with real-world data from complex disorders, such as Parkinson's disease (PD). Our study addresses this gap by examining the application of various GNN models, including Graph Convolutional Network, ChebyNet, and Graph Attention Network, to identify and interpret discriminative patterns between PD patients and controls using omics data. The developed pipeline integrates Lasso penalty-based feature selection, similarity graph construction, and final modeling for sample classification. Through an end-to-end model building and evaluation process, we assess the practical utility of the pipeline on independent PD omics datasets. Overall, our analyses highlight some of the benefits and challenges of using graph structure data for machine learning analysis of disease-related omics data and provide directions for further research.
Research centres :	Luxembourg Centre for Systems Biomedicine (LCSB): Biomedical Data Science (Glaab Group)
Funders :	Fonds National de la Recherche - FnR
Name of the research project :	R-AGR-0621 > Dons Alzheimer Projekt (Dr. Glaab) > 26/10/2015 - 19/01/2048 > SCHNEIDER Reinhard
Target :	Researchers ; Professionals ; Students ; General public
Permalink :	http://hdl.handle.net/10993/56048
FnR project :	FnR ; FNR14599012 > Enrico Glaab > DIGIPD > Validating Digital Biomarkers For Better Personalized Treatment Of Parkinson’S Disease > 01/05/2021 > 30/04/2024 > 2020

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	Fulltext file(s): File Commentary Version Size Access Open access ISMBECCB2023_1251_GomezdeLope_poster.pdf Publisher postprint 12.93 MB View/Open

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