Reference : Results and Lessons Learned from the sbv IMPROVER Metagenomics Diagnostics for Inflam...

	Document type :	Scientific journals : Article
	Discipline(s) :	Life sciences : Multidisciplinary, general & others Human health sciences : Immunology & infectious disease Human health sciences : Multidisciplinary, general & others
	Focus Areas :	Systems Biomedicine
	To cite this reference:	http://hdl.handle.net/10993/54803

Title :	Results and Lessons Learned from the sbv IMPROVER Metagenomics Diagnostics for Inflammatory Bowel Disease Challenge
Language :	English
Author, co-author :	Khachatryan, Lusine [> >]
	Xiang, Yang [> >]
	Ivanov, Artem [> >]
	Glaab, Enrico [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Biomedical Data Science]
	Graham, Garrett [> >]
	Granata, Ilaria [> >]
	Giordano, Maurizio [> >]
	Maddalena, Lucia [> >]
	Piccirillo, Marina [> >]
	Manipur, Ichcha [> >]
	Baruzzo, Giacomo [> >]
	Cappellato, Marco [> >]
	Avot, Batiste [> >]
	Stan, Adrian [> >]
	Battey, James [> >]
	Lo Sasso, Giuseppe [> >]
	Boue, Stephanie [> >]
	Ivanov, Nikolai V. [> >]
	Peitsch, Manuel C. [> >]
	Hoeng, Julia [> >]
	Falquet, Laurent [> >]
	Di Camillo, Barbara [> >]
	Guarracino, Mario [> >]
	Ulyantsev, Vladimir [> >]
	Sierro, Nicolas [> >]
	Poussin, Carine [> >]
Publication date :	2023
Journal title :	Scientific Reports
Publisher :	Nature Publishing Group
Volume :	in press
Peer reviewed :	Yes
Audience :	International
Keywords :	[en] Gut microbiota ; Inflammatory bowel disease ; Metagenomics ; Non-invasive diagnostics ; Diagnosis ; Crohn's Disease ; Ulcerative Colitis ; classification ; supervised ; machine learning
Abstract :	[en] A growing body of evidence links gut microbiota changes with inflammatory bowel disease (IBD), raising the potential benefit of exploiting metagenomics data for non-invasive IBD diagnostics. The sbv IMPROVER Metagenomics Diagnosis for Inflammatory Bowel Disease Challenge (MEDIC) investigated computational metagenomics methods for discriminating IBD and nonIBD subjects. Participants in this challenge were given independent training and test metagenomics data from IBD and nonIBD subjects, which could be wither either raw read data (sub-challenge 1, SC1) or processed taxonomy- and function-based profiles (sub-challenge 2, SC2). A total of 81 anonymized submissions were received between September 2019 and March 2020. Most participants’ predictions performed better than random predictions in classifying IBD vs nonIBD, Ulcerative Colitis (UC) vs nonIBD, and Crohn’s Disease (CD) vs nonIBD. However, discrimination between UC and CD remains challenging, with the classification quality similar to the set of random predictions. We analyzed the class prediction accuracy, the metagenomics features by the teams, and computational methods used. These results will be openly shared with the scientific community to help advance IBD research and illustrate the application of a range of computational methodologies for effective metagenomic classification.
Research centres :	Luxembourg Centre for Systems Biomedicine (LCSB): Biomedical Data Science (Glaab Group)
Target :	Researchers ; Professionals ; Students
Permalink :	http://hdl.handle.net/10993/54803
DOI :	10.1038/s41598-023-33050-0
Other URL :	https://doi.org/10.1038/s41598-023-33050-0
Mentions required by the publisher for OA :	The original publication is available at: https://doi.org/10.1038/s41598-023-33050-0

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