Author

Joaquín Federico Mould-Quevedo

Seqirus (United States)ORCID 0000-0003-4847-5956Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:machine learning (classical)· 2 Research Focus:ecology & biodiversity· 1 Organism:multiple species· 1 Research Focus:variant effect prediction· 1 Assay:whole-genome sequencing· 1

Frequent Co-Authors

Liam BrierleyiD· 2 shared Matthew BaylisiD· 2 shared Christl Ann DonnellyiD· 1 shared Joe HiltoniD· 1 shared Sarah E. HayesiD· 1 shared

Activity by Year

2025-2025 · peak 2/yr

Co-Author Collaboration Network

5 top collaborators · 10 mutual co-author links

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2 shared papersEcology

Liam BrierleyORCID

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2 papers

Recent·Most cited

EcologyScientific ReportsPeer-reviewedDec 2, 2025

Ecology and environment predict spatially stratified risk of H5 highly pathogenic avian influenza clade 2.3.4.4b in wild birds across Europe

Sarah E. Hayes, Joe Hilton, Joaquín Federico Mould-Quevedo…·Liam Brierley

The paper uses machine learning to model the spread of H5N1 avian influenza in Europe, finding that ecological factors refine risk predictions beyond climate alone.

12 citationstop 2%

multiple speciesmachine learning (classical)

𝕏 PostBluesky

GenomicsbioRxivPreprintSep 16, 2025

An AI for an AI: identifying zoonotic potential of avian influenza viruses via genomic machine learning

Liam Brierley, Joaquín Federico Mould-Quevedo, Matthew Baylis

The paper develops a machine learning model to predict zoonotic potential of avian influenza viruses using genomic data, achieving high accuracy in distinguishing zoonotic from non-zoonotic strains.

3 citations

whole-genome sequencingmachine learning (classical)

𝕏 PostBluesky