Author

F. Fournier

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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Ecology· 1 Immunology· 1 Microbiology· 1

Signature Assays, Organisms & Methods

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Research Focus:development & morphogenesis· 2 Approach:experimental assay· 2 Research Focus:host-pathogen & immune response· 2 Organism:bacteria (other)· 1 Research Focus:cell signaling & trafficking· 1 Research Focus:differential expression· 1 Organism:Drosophila· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

A. Bush-Beaupre· 1 shared A. Dejda· 1 shared A. Droit· 1 shared A. MacDonald· 1 shared A. Wilson· 1 shared A.-M. Fortier· 1 shared C. Dauly· 1 shared C. Gotti· 1 shared

Activity by Year

2019-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 12 mutual co-author links

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Curved chords = mutual co-authorship

1 shared paperEcology

A. Bush-Beaupre

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Corticosteroids prevent pathological angiogenesis yet compromise reparative vascular remodeling in the retina

M. Hata, M. Hata, A. Dejda, F. Pilon, R. Diaz, F. Fournier, J.-S. Joyal, G. Cagnone, Y. Ochi, S. Crespo-Garcia, A. Wilson, P. Sapieha

This study shows corticosteroids inhibit early pathological angiogenesis but hinder reparative vascular remodeling in retinopathy.

bioRxiv · Jun 6, 2024Immunology

REPRODUCTIVE COMPATIBILITY OF TWO LINES OF DELIA PLATURA (DIPTERA: ANTHOMYIIDAE)

A. Bush-Beaupre, M. Belisle, A.-M. Fortier, F. Fournier, A. MacDonald, J. Savage

This study investigates reproductive compatibility between two genetic lines of Delia platura, finding evidence of pre-mating isolation in one direction but not the other, and partial post-mating isolation.

bioRxiv · Aug 1, 2023 · PreprintEcology

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