Author

Yann Guitton

Institut National de Recherche pour l'Agriculture, l'Alimentation et l'EnvironnementORCID 0000-0002-4479-0636Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 3

Signature Assays, Organisms & Methods

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Approach:machine learning (classical)· 2 Assay:metabolomics· 2 Research Focus:benchmarking· 1 Research Focus:database / resource· 1 Approach:experimental assay· 1 Organism:human· 1 Assay:metagenomics· 1 Research Focus:software / tool· 1

Frequent Co-Authors

Bruno Le BizeciD· 2 shared Aaron FaitiD· 1 shared Adolfo AmézquitaiD· 1 shared Aleksandr Alekseevich SmirnoviD· 1 shared Alexander A. AksenoviD· 1 shared Alexey V. MelnikiD· 1 shared Alisdair Robert FernieiD· 1 shared Amina BouslimaniiD· 1 shared

Activity by Year

2019-2024 · peak 1/yr

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10 top collaborators · 45 mutual co-author links

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2 shared papersBioinformatics

Bruno Le BizecORCID

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3 papers

Recent·Most cited

PhysioFit: a software to quantify cell growth parameters and extracellular fluxes

Loïc Le Grégam, Yann Guitton, Floriant Bellvert, Stéphanie Heux, Fabien Jourdan, Jean‐Charles Portais, Pierre Millard

PhysioFit quantifies cell growth parameters and extracellular fluxes using flexible growth models and provides a user-friendly software tool for non-computational biologists.

Bioinformatics · Jul 29, 2024Peer-reviewedBioinformatics

Algorithmic Learning for Auto-deconvolution of GC-MS Data to Enable Molecular Networking within GNPS.

Alexander A. Aksenov, Ivan Laponogov, Zheng Zhang, Sophie L. F. Doran, Ilaria Belluomo, Dennis A. Veselkov, Wout Bittremieux, Louis-Félix Nothias-Scaglia, Mélissa Nothias-Esposito, Katherine N. Maloney, Biswapriya B. Misra, Alexey V. Melnik, Aleksandr Alekseevich Smirnov, Xiuxia Du, Kenneth Lyons Jones, , Kirill Veselkov

The paper develops a machine learning workflow for auto-deconvolution of GC-MS data within GNPS, enabling molecular networking and applications in breathomics and volatilome mapping.

Nature Biotechnology · Nov 9, 2020Peer-reviewedBioinformatics

WiPP: Workflow for improved Peak Picking for Gas Chromatography-Mass Spectrometry (GC-MS) data

Nico Borgsmüller, Yoann Gloaguen, Tobias Opialla, Eric Blanc, Emilie Sicard, Anne‐Lise Royer, Bruno Le Bizec, Stéphanie Durand, Carole Migné, Mélanie Pétéra, Estelle Pujos‐Guillot, Franck Giacomoni, Yann Guitton, Dieter Beule, Jennifer Kirwan

WiPP improves GC-MS peak detection by combining multi-algorithm results with machine learning-based quality assessment, enhancing accuracy and enabling algorithm comparison.

Metabolites · Aug 21, 2019Peer-reviewedBioinformatics