Author

F. Roux-Dalvai

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

Biology Disciplines

Click to filter author papers

Microbiology· 2

Signature Assays, Organisms & Methods

Click to filter · or open tag

Research Focus:differential expression· 2 Assay:proteomics· 2 Organism:bacteria (other)· 1 Research Focus:cell type annotation· 1 Approach:experimental assay· 1 Research Focus:host-pathogen & immune response· 1 Approach:machine learning (classical)· 1 Organism:mouse· 1

Frequent Co-Authors

A. Droit· 2 shared B. Muselius· 1 shared C. Dauly· 1 shared C. Gotti· 1 shared F. Fournier· 1 shared I. Kelly· 1 shared J. Bestman-Smith· 1 shared J. Geddes-McAlister· 1 shared

Activity by Year

2019-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 31 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 papersMicrobiology

A. Droit

Mutual co-authors in cluster

Author profile →

2 papers

Recent·Most cited

Resolving the temporal splenic proteome during fungal infection for discovery of putative dual perspective biomarker signatures

B. Muselius, F. Roux-Dalvai, A. Droit, J. Geddes-McAlister

This study uses proteomics to analyze the temporal spleen proteome during Cryptococcus neoformans infection in mice, identifying immune and pathogen biomarkers.

Journal of the American Society for Mass Spectrometry · Sep 6, 2023Peer-reviewedMicrobiology

Fast and accurate bacterial species identification in biological samples using LC-MS/MS mass spectrometry and machine learning

F. Roux-Dalvai, C. Gotti, M. Leclercq, M.-C. Helie, M. Boissinot, T. N. Arrey, C. Dauly, F. Fournier, I. Kelly, J. Marcoux, J. Bestman-Smith, M. G. Bergeron, A. Droit

This paper develops a method using LC-MS/MS and machine learning to rapidly identify bacterial species in clinical samples without culture, achieving 97% accuracy for UTI pathogens.

bioRxiv · May 10, 2019Microbiology