Author

L. Irazu

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

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 2 Assay:proteomics· 2 Approach:deep learning· 1 Organism:E. coli· 1 Organism:human· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

J. Zintgraff· 2 shared C. Carbonari· 1 shared C. S. Lara· 1 shared E. Manfredi· 1 shared E. Miliwebsky· 1 shared F. Rocca· 1 shared I. Chinen· 1 shared M. F. Rocca· 1 shared

Activity by Year

2022-2022 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 27 mutual co-author links

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2 shared papersMicrobiology

J. Zintgraff

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2 papers

Recent·Most cited

A la carte, Streptococcus pneumoniae capsular typing: using MALDI-TOF mass spectrometry and machine learning algorithms as complementary tools for the determination of PCV13 serotypes and the most prevalent NON PCV13 serotypes according to Argentina's epidemiology.

J. Zintgraff, M. F. Rocca, N. S. Eluchans, L. Irazu, M. Moscoloni, C. S. Lara, M. Santos

This paper evaluates MALDI-TOF MS combined with machine learning for serotyping S. pneumoniae isolates in Argentina, achieving moderate accuracy in identifying PCV13 and non-PCV13 serotypes.

bioRxiv · Dec 22, 2022 · PreprintMicrobiology

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