Author

Clarisse Gotti

Centre de recherche du CHU de Québec-Université LavalORCID 0000-0001-8316-7030Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2 Microbiology· 1 Neuroscience· 1

Signature Assays, Organisms & Methods

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Assay:proteomics· 3 Research Focus:differential expression· 2 Organism:human· 2 Approach:machine learning (classical)· 2 Approach:statistical model· 2 Organism:bacteria (other)· 1 Research Focus:benchmarking· 1 Research Focus:cell-cell communication· 1

Frequent Co-Authors

Arnaud DroitiD· 3 shared Florence Roux‐DalvaiiD· 3 shared Mickaël LeclercqiD· 3 shared Antoine Lacombe-Rastoll· 2 shared Ève Bérubé· 2 shared Arthur BorderieiD· 1 shared Benjamin MorilloniD· 1 shared Charles Joly-BeauparlantiD· 1 shared

Activity by Year

2021-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 25 mutual co-author links

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3 shared papersBioinformatics

Arnaud DroitORCID

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4 papers

Recent·Most cited

BioinformaticsbioRxivPreprintJun 9, 2026

Pairing Data Independent Acquisition and High-Resolution Full Scan for Fast Urinary Tract Infection Diagnosis

Elloise Coyle, Antoine Lacombe-Rastoll, Florence Roux‐Dalvai…·Arnaud Droit

This paper presents a method combining DIA MS/MS and MS1-only spectra for rapid UTI pathogen identification, achieving high accuracy in both controlled and clinical samples.

DOI 10.64898/2026.03.09.710302

humanproteomics

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NeurosciencebioRxivPreprintJun 5, 2025

Auditory cortical coding of spectrotemporal modulations is sufficient to support perceptual categorization of speech and music

Jérémie Ginzburg, Clarisse Gotti, Émilie Cloutier Debaque…·Philippe Albouy

DOI 10.1101/2025.06.05.657930

humanelectronic health records

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MicrobiologyMolecular & Cellular ProteomicsPeer-reviewedAug 22, 2024

LC-SRM combined with machine learning enables fast identification and quantification of bacterial pathogens in urinary tract infections

Clarisse Gotti, Florence Roux‐Dalvai, Ève Bérubé…·Arnaud Droit

This paper develops a method combining LC-SRM and machine learning to rapidly identify and quantify bacterial pathogens in UTIs with high accuracy.

5 citations

bacteria (other)proteomics

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BioinformaticsJournal of Proteome ResearchPeer-reviewedSep 2, 2021

Extensive and accurate benchmarking of DIA acquisition methods and software tools using a complex proteomic standard

Clarisse Gotti, Florence Roux‐Dalvai, Charles Joly-Beauparlant…·Arnaud Droit

The paper benchmarks DIA acquisition methods and software tools using a complex proteomic standard, identifying optimal workflows for LC-MS/MS proteomic analyses.

89 citationstop 2%

E. coliproteomics

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