Author

Lucía Schmidt-Santiago

Universidad Carlos III de MadridORCID 0009-0005-5754-4441Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Microbiology· 2 Bioengineering· 1

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 3 Assay:metagenomics· 3 Approach:deep learning· 2 Approach:statistical model· 2 Research Focus:variant calling· 2 Organism:C. elegans· 1 Research Focus:clustering· 1 Research Focus:differential expression· 1

Frequent Co-Authors

Alejandro Guerrero-LópeziD· 3 shared Belén Rodríguez‐SáncheziD· 3 shared Carlos Sevilla-SalcedoiD· 3 shared David Rodríguez‐TemporaliD· 3 shared Mario Blázquez‐SáncheziD· 2 shared Vanessa Gómez-VerdejoiD· 2 shared Inés López-Mareca· 1 shared José Miguel MorenoiD· 1 shared

Activity by Year

2024-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 38 mutual co-author links

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3 shared papersMicrobiology

Alejandro Guerrero-LópezORCID

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3 papers

Recent·Most cited

BioengineeringBriefings in BioinformaticsPeer-reviewedApr 17, 2026

Machine Learning applied to MALDI-TOF data in a clinical setting: a systematic review

Lucía Schmidt-Santiago, Alejandro Guerrero-López, Carlos Sevilla-Salcedo…·Vanessa Gómez-Verdejo

This review evaluates ML applications for MALDI-TOF MS in clinical microbiology, identifying trends and challenges in bacterial identification, resistance prediction, and strain typing.

12 citationstop 1%

metagenomicsstatistical model

𝕏 PostBluesky

MicrobiologybioRxivPreprintMay 31, 2025

MARISMa: a routine MALDI-TOF MS database from 2018 to 2024 from Spain

Lucía Schmidt-Santiago, Inés López-Mareca, Mario Blázquez‐Sánchez…·Alejandro Guerrero-López

The paper introduces a large MALDI-TOF MS dataset from Spain to support AI-driven clinical microbiology research and improve antimicrobial resistance detection.

5 citations

metagenomicsdeep learning

𝕏 PostBluesky

MicrobiologybioRxivPreprintOct 29, 2024

Robust machine learning for Clostridioides difficile typing from mass spectrometry via data augmentation

Alejandro Guerrero-López, Mario Blázquez‐Sánchez, Lucía Bravo-Antón…·Vanessa Gómez-Verdejo

This study improves ML-based C. difficile typing from MALDI-TOF MS spectra by using data augmentation to reduce performance variability across different experimental conditions.

1 citation

C. elegansmetagenomics

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