Author

Guillaume Cabanac

Centre National de la Recherche ScientifiqueORCID 0000-0003-3060-6241Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Cancer Biology· 1 Genetics· 1

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Organism:human· 2 Approach:statistical model· 2 Organism:Arabidopsis· 1 Research Focus:database / resource· 1 Approach:experimental assay· 1 Assay:metabolomics· 1 Organism:plant (other)· 1

Frequent Co-Authors

Amanda Capes‐DavisiD· 2 shared Cyril LabbéiD· 2 shared Jennifer Anne ByrneiD· 2 shared Pranujan PathmendraiD· 2 shared Thomas StoegeriD· 2 shared Aaron Lee· 1 shared Aurélien Amiel· 1 shared Bernard DumasiD· 1 shared

Activity by Year

2020-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 27 mutual co-author links

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2 shared papersCancer Biology

Amanda Capes‐DavisORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Cancer BiologyInternational Journal of CancerPeer-reviewedMay 15, 2024

Misspellings or miscellings- non-verifiable cell lines in cancer research publications

Danielle J. Oste, Pranujan Pathmendra, Reese A. K. Richardson…·Jennifer Anne Byrne

The paper identifies non-verifiable human cell lines in cancer research publications, finding 235 papers referring to 7 cell lines as independent entities despite lacking proper documentation.

26 citationstop 4%

humanexperimental assay

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GeneticsLife Science AlliancePeer-reviewedJan 12, 2022

Human gene function publications that describe wrongly identified nucleotide sequence reagents are unacceptably frequent within the genetics literature

Yasunori Park, Rachael A. West, Pranujan Pathmendra…·Jennifer Anne Byrne

The paper screens gene function literature for nucleotide sequence errors, finding 712 papers with at least one wrongly identified sequence, which may misinform future therapies.

37 citationstop 4%

humanstatistical model

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BioinformaticsAnalytical ChemistryPeer-reviewedJun 26, 2020

MS-CleanR: A feature-filtering approach to improve annotation rate in untargeted LC-MS based metabolomics

Ophélie Fraisier, Justine Chervin, Guillaume Cabanac…·Guillaume Marti

MS-CleanR improves metabolite annotation rates in untargeted LC-MS by reducing redundant signals while retaining most unique metabolites, enhancing analysis of plant metabolomes.

119 citationstop 3%

Arabidopsisplant (other)

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