Author

Etienne Thévenot

Commissariat à l'Énergie Atomique et aux Énergies AlternativesORCID 0000-0003-1019-4577Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

Click to filter author papers

Bioinformatics· 3

Signature Assays, Organisms & Methods

Click to filter · or open tag

Assay:metabolomics· 3 Approach:benchmark study· 1 Research Focus:benchmarking· 1 Research Focus:database / resource· 1 Research Focus:differential expression· 1 Approach:experimental assay· 1 Approach:graph neural network· 1 Approach:statistical model· 1

Frequent Co-Authors

Alejandra N Gonzalez-BeltraniD· 2 shared Anders O. LarssoniD· 2 shared Carles FoguetiD· 2 shared Christoph RuttkiesiD· 2 shared Christoph SteinbeckiD· 2 shared Daniel SchoberiD· 2 shared David JohnsoniD· 2 shared Gianluigi ZanettiiD· 2 shared

Activity by Year

2018-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersBioinformatics

Alejandra N Gonzalez-BeltranORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

MassGAT: a graph-based collective learning approach for untargeted detection and annotation of LC-MS data

Paul-Henri Pinart, Annelaure Damont, Sylvain Déchaumet, Etienne Thévenot

MassGAT improves LC-HRMS data analysis by combining peak detection and annotation using a graph-based approach, outperforming existing methods on real datasets.

bioRxiv · Aug 2, 2026 · PreprintBioinformatics

Interoperable and scalable data analysis with microservices: Applications in Metabolomics

Payam Emami Khoonsari, Pablo A Moreno, Sven Bergmann, Joachim Burman, Marco Capuccini, Matteo Carone, Marta Cascante, Pedro de Atauri, Carles Foguet, Alejandra N Gonzalez-Beltran, Thomas Hankemeier, Kenneth Haug, Sijin He, Stephanie Herman, David Johnson, , Ola Spjuth

The paper introduces a microservices-based framework for scalable data analysis, validated in metabolomics, enabling dynamic scaling and integration of software tools.

Bioinformatics · Mar 8, 2019Peer-reviewedBioinformatics

PhenoMeNal: Processing and analysis of Metabolomics data in the Cloud

Kristian Peters, James Bradbury, Sven Bergmann, Marco Capuccini, Marta Cascante, Pedro de Atauri, Timothy M. D. Ebbels, Carles Foguet, Robert Charles Glen, Alejandra N Gonzalez-Beltran, Ulrich L. Günther, Evangelos Handakas, Thomas Hankemeier, Kenneth Haug, Stephanie Herman, , Christoph Steinbeck

PhenoMeNal provides a cloud-based platform for metabolomics data analysis, integrating tools and workflows to enable reproducible and scalable research.

GigaScience · Dec 7, 2018Peer-reviewedBioinformatics