Author

Antoine Lacombe-Rastoll

Centre de recherche du CHU de Québec-Université Laval

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 1 Microbiology· 1

Signature Assays, Organisms & Methods

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Research Focus:differential expression· 2 Approach:machine learning (classical)· 2 Assay:proteomics· 2 Organism:bacteria (other)· 1 Organism:human· 1

Frequent Co-Authors

Arnaud DroitiD· 2 shared Clarisse GottiiD· 2 shared Ève Bérubé· 2 shared Florence Roux‐DalvaiiD· 2 shared Mickaël LeclercqiD· 2 shared Cláudia P.B. Martins· 1 shared Cristina C. JacobiD· 1 shared Dorte B. Bekker‐JenseniD· 1 shared

Activity by Year

2024-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 39 mutual co-author links

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2 shared papersBioinformatics

Arnaud DroitORCID

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2 papers

Recent·Most cited

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

Elloise Coyle, Antoine Lacombe-Rastoll, Florence Roux‐Dalvai, Mickaël Leclercq, Pascaline Bories, Ève Bérubé, Clarisse Gotti, Dorte B. Bekker‐Jensen, Nicolai Bache, Sandra Isabel, 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.

bioRxiv · Jun 9, 2026 · PreprintBioinformatics

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, Antoine Lacombe-Rastoll, Mickaël Leclercq, Cristina C. Jacob, Maurice Boissinot, Cláudia P.B. Martins, Neloni R. Wijeratne, Michel G. Bergeron, 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.

Molecular & Cellular Proteomics · Aug 22, 2024Peer-reviewedMicrobiology