Reference : Machine learning applied to higher order functional representations of omics data rev...

	Document type :	Scientific congresses, symposiums and conference proceedings : Poster
	Discipline(s) :	Life sciences : Biotechnology Life sciences : Multidisciplinary, general & others Engineering, computing & technology : Multidisciplinary, general & others Human health sciences : Neurology Human health sciences : Multidisciplinary, general & others
	Focus Areas :	Systems Biomedicine
	To cite this reference:	http://hdl.handle.net/10993/52061

Title :	Machine learning applied to higher order functional representations of omics data reveals biological pathways associated with Parkinson‘s Disease
Language :	English
Translated title :	[en] Machine learning applied to higher order functional representations of omics data reveals biological pathways associated with Parkinson‘s Disease
Author, co-author :	Gómez de Lope, Elisa [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Biomedical Data Science >]
	Glaab, Enrico [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Biomedical Data Science >]
Publication date :	18-Sep-2022
Number of pages :	A0 sized poster
Peer reviewed :	Yes
Audience :	International
Event name :	European Conference on Computational Biology - European Student Council Symposium
Event date :	18/09/2022 - 21/09/2022
Event organizer :	European Conference on Computational Biology,
Event place (city) :	Sitges, Barcelona
Event country :	Spain
Keywords :	[en] machine learning ; omics data ; Parkinson Disease ; pathways ; Pathifier
Abstract :	[en] Background: Despite the increasing prevalence of Parkinson’s Disease (PD) and research efforts to understand its underlying molecular pathogenesis, early diagnosis of PD remains a challenge. Machine learning analysis of blood-based omics data is a promising non-invasive approach to finding molecular fingerprints associated with PD that may enable an early and accurate diagnosis. Description: We applied several machine learning classification methods to public omics data from PD case/control studies. We used aggregation statistics and Pathifier’s pathway deregulation scores to generate higher order functional representations of the data such as pathway-level features. The models’ performance and most relevant predictive features were compared with individual feature level predictors. The resulting diagnostic models from individual features and Pathifier’s pathway deregulation scores achieve significant Area Under the Curve (AUC, a receiver operating characteristic curve) scores for both cross-validation and external testing. Furthermore, we identify plausible biological pathways associated with PD diagnosis. Conclusions: We have successfully built machine learning models at pathway-level and single-feature level to study blood-based omics data for PD diagnosis. Plausible biological pathway associations were identified. Furthermore, we show that pathway deregulation scores can serve as robust and biologically interpretable predictors for PD.
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 :	FNR14599012
Target :	Researchers ; Professionals ; Students
Permalink :	http://hdl.handle.net/10993/52061
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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