Author

C. Gotti

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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Microbiology· 2 Bioengineering· 1

Signature Assays, Organisms & Methods

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Organism:bacteria (other)· 2 Research Focus:differential expression· 2 Approach:machine learning (classical)· 2 Assay:proteomics· 2 Research Focus:cell type annotation· 1 Approach:experimental assay· 1 Assay:imaging· 1

Frequent Co-Authors

A. Droit· 2 shared F. Roux-Dalvai· 2 shared M. Boissinot· 2 shared M. G. Bergeron· 2 shared M. Leclercq· 2 shared A. Lacombe-Rastoll· 1 shared A. Sensini· 1 shared A. Zucchelli· 1 shared

Activity by Year

2019-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 27 mutual co-author links

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2 shared papersMicrobiology

A. Droit

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

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

C. Gotti, F. Roux-Dalvai, E. Berube, A. Lacombe-Rastoll, M. Leclercq, C. C. Jacob, M. Boissinot, C. Martins, N. R. Wijeratne, M. G. Bergeron, A. Droit

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

bioRxiv · May 31, 2024Microbiology

Full-field strain distribution in hierarchical electrospun nanofibrous Poly-L(lactic) acid and Collagen based scaffolds for tendon and ligament tissue regeneration: a multiscale study

A. Sensini, O. Stamati, G. Marchiori, N. Sancisi, C. Gotti, G. Giavaresi, L. Cristofolini, M. L. Focarete, A. Zucchelli, G. Tozzi

This study develops hierarchical electrospun scaffolds mimicking tendon and ligament structures and measures their strain distribution using multiscale analysis.

bioRxiv · Jun 1, 2023Bioengineering

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