Author

M. J. Arroyo

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· 5

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 4 Approach:machine learning (classical)· 4 Assay:proteomics· 4 Research Focus:clustering· 3 Organism:bacteria (other)· 2 Research Focus:differential expression· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1

Frequent Co-Authors

L. Mancera· 5 shared P. Munoz· 5 shared B. Rodriguez-Sanchez· 4 shared D. Rodriguez-Temporal· 4 shared G. Mendez· 4 shared A. Candela· 3 shared A. Guerrero-Lopez· 2 shared E. Cercenado· 2 shared

Activity by Year

2020-2024 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 42 mutual co-author links

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5 shared papersMicrobiology

L. Mancera

Mutual co-authors in cluster

Author profile →

5 papers

Recent·Most cited

Automated web-based typing of Clostridioides difficile ribotypes via MALDI-TOF MS

M. Sanchez-Blazquez, A. Guerrero-Lopez, A. Candela, A. Belenguer-Llorens, J. M. Moreno, C. Sevilla Salcedo, M. Sanchez-Cueto, M. J. Arroyo, M. Gutierrez-Pareja, V. Gomez-Verdejo, P. M. Olmos, L. Mancera, P. Munoz, M. Marin, L. Alcala, , B. Rodriguez Sanchez

This paper develops automated tools using MALDI-TOF MS and machine learning to accurately classify C. difficile ribotypes from clinical isolates.

bioRxiv · Jan 24, 2024 · PreprintMicrobiology

Identification of Mycobacterium abscessus subspecies by MALDI-TOF Mass Spectrometry and Machine Learning

D. Rodriguez-Temporal, L. Herrera-Leon, F. Alcaide, D. Domingo, N. Vila-Olmo, M. J. Arroyo, G. Mendez, P. Munoz, L. Mancera, M. J. Ruiz-Serrano, B. Rodriguez-Sanchez

This study uses MALDI-TOF MS and machine learning to accurately identify subspecies of Mycobacterium abscessus complex with 95.8% accuracy.

bioRxiv · Jul 28, 2022Microbiology

Automatic discrimination of species within the Enterobacter cloacae complex using MALDI-TOF Mass Spectrometry and supervised algorithms

A. Candela, A. Guerrero-Lopez, M. Mateos, A. Gomez Asenjo, M. J. Arroyo, M. Hernandez-Garcia, R. del Campo, E. Cercenado, A. Cuenod, G. Mendez, L. Mancera, J. d. D. Caballero, L. Martinez-Garcia, D. Gijon, M. I. Morosini, , B. Rodriguez-Sanchez

This paper develops machine learning models using MALDI-TOF MS data to accurately differentiate species within the Enterobacter cloacae complex.

bioRxiv · Nov 2, 2021 · PreprintMicrobiology

Rapid and reproducible MALDI-TOF-based method for detection Vancomycin-resistant Enterococcus faecium using classifying algorithms

A. Candela, M. J. Arroyo, A. Sanchez-Molleda, G. Mendez, D. Rodriguez-Temporal, L. Quiroga, A. Ruiz, E. Cercenado, M. Marin, P. Munoz, L. Mancera, B. Rodriguez-Sanchez

This study evaluates MALDI-TOF MS for detecting vancomycin-resistant Enterococcus faecium using classification algorithms, achieving 77.53-86.65% accuracy in distinguishing resistance types.

bioRxiv · Jun 23, 2021Microbiology

Discrimination among Cryptococcus deneoformans, C. neoformans and interspecies hybrids using MALDI-TOF Mass Spectrometry

M. E. Zvezdanova, M. J. Arroyo, G. Mendez, J. Guinea, L. Mancera, P. Munoz, B. Rodriguez-Sanchez, P. Escribano

This study uses MALDI-TOF MS with an in-house library to distinguish between Cryptococcus species and hybrids, achieving 100% accuracy for two species but not hybrids.

bioRxiv · Mar 13, 2020 · PreprintMicrobiology