Author

M. Prieto

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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Immunology· 1 Microbiology· 1 Scientific Communication & Education· 1

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 2 Assay:proteomics· 2 Research Focus:benchmarking· 1 Research Focus:database / resource· 1 Approach:deep learning· 1 Organism:E. coli· 1 Approach:machine learning (classical)· 1 Organism:mouse· 1

Frequent Co-Authors

B. Lopez· 1 shared B. Rearte· 1 shared C. Carbonari· 1 shared C. Vay· 1 shared D. Murphy· 1 shared E. Manfredi· 1 shared E. Miliwebsky· 1 shared E. Schultes· 1 shared

Activity by Year

2018-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 14 mutual co-author links

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1 shared paperImmunology

B. Lopez

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Rapid and accurate identification of Escherichia coli STEC O157:H7 by mass spectrometry, artificial intelligence and detection of specific biomarkers peaks.

E. Manfredi, F. Rocca, J. Zintgraff, L. Irazu, E. Miliwebsky, C. Carbonari, N. Deza, M. Prieto, I. Chinen

This paper develops a method using mass spectrometry, biomarkers, and AI to accurately identify STEC O157:H7 from other diarrheagenic E. coli with high sensitivity and specificity.

bioRxiv · Mar 31, 2022 · PreprintMicrobiology

Peptidome profiling for the immunological stratification in sepsis: a proof of concept study

M. Ledesma, M. F. Todero, L. Maceira, M. Prieto, C. Vay, M. Galas, B. Lopez, N. Yokobori, B. Rearte

This study uses plasma peptidome profiling with MALDI-TOF mass spectrometry and machine learning to stratify sepsis phases in mice, achieving high sensitivity and specificity.

bioRxiv · Dec 29, 2020Immunology

Evaluating FAIR-Compliance Through an Objective, Automated, Community-Governed Framework

M. D. Wilkinson, M. Dumontier, S.-A. Sansone, L. O. Bonino da Silva Santos, M. Prieto, J. Gautier, P. McQuilton, D. Murphy, M. Crosas, E. Schultes

The paper introduces FAIR Evaluator, a tool to assess FAIRness of digital resources, and compares automated vs manual evaluations across repositories.

bioRxiv · Sep 25, 2018 · PreprintScientific Communication & Education