Author

María Vinaixa

Instituto de Salud Carlos IIIORCID 0000-0001-9804-0171Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Assay:metabolomics· 3 Research Focus:differential expression· 2 Organism:human· 2 Approach:deep learning· 1 Organism:E. coli· 1 Approach:experimental assay· 1 Organism:mouse· 1 Research Focus:software / tool· 1

Frequent Co-Authors

Óscar YanesiD· 3 shared Roger GinéiD· 3 shared Jordi CapelladesiD· 2 shared Josep M. BadiaiD· 2 shared Abubaker PataniD· 1 shared Alexandra JunzaiD· 1 shared Andrea M. BrunneriD· 1 shared Anna NovialsiD· 1 shared

Activity by Year

2021-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

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3 shared papersBioinformatics

Óscar YanesORCID

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3 papers

Recent·Most cited

Solid Ionic Matrices applied via Low-Temperature Evaporation enable High-Resolution and Sensitive MALDI Imaging of Metabolites

Toufik Mahamdi, Nadia Moltó, Mireia Arasa, Roger Giné, Jordi Rofes, María García‐Altares, María Vinaixa, Óscar Yanes

This paper introduces a method using solid ionic matrices with low-temperature evaporation to improve MALDI-MSI sensitivity and resolution for small metabolites, achieving detection of 73 metabolites in mouse brain.

Journal of the American Society for Mass Spectrometry · Sep 3, 2026Peer-reviewedBioengineering

ChemEmbed: A deep learning framework for metabolite identification using enhanced MS/MS data and multidimensional molecular embeddings

Muhammad Faizan-Khan, Roger Giné, Josep M. Badia, Maribel Pérez-Ribera, Manuel Ruíz-Botella, Alexandra Junza, Jordi Capellades, Iván Pérez-López, Shipei Xing, Abubaker Patan, Laura Brugnara, Anna Novials, Joan‐Marc Servitja, María Vinaixa, Pieter C. Dorrestein, , Óscar Yanes

ChemEmbed improves metabolite identification by combining enhanced MS/MS data with multidimensional molecular embeddings, achieving high accuracy in benchmark tests.

Briefings in Bioinformatics · Jan 1, 2026Peer-reviewedBioinformatics

HERMES: a molecular formula-oriented method to target the metabolome

Roger Giné, Jordi Capellades, Josep M. Badia, Dennis Vughs, Michaela Schwaiger-Haber, María Vinaixa, Andrea M. Brunner, Gary J. Patti, Óscar Yanes

HERMES improves metabolite identification and structural annotation in mass spectrometry by using molecular formulas and avoiding peak detection.

bioRxiv · Mar 8, 2021 · PreprintBioinformatics