Author

Francesco Durazzi

University of BolognaORCID 0000-0001-8729-6438Unclaimed 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 Approach:deep learning· 1 Organism:human· 1 Assay:metagenomics· 1 Assay:protein sequence· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

Daniel RemondiniiD· 2 shared Anis Mansouri· 1 shared Dominique SwenneniD· 1 shared Marion KoopmansiD· 1 shared Rina MekuliiD· 1 shared Ron A. M. FouchieriD· 1 shared

Activity by Year

2025-2026 · peak 1/yr

Co-Author Collaboration Network

6 top collaborators · 9 mutual co-author links

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2 shared papersBioinformatics

Daniel RemondiniORCID

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2 papers

Recent·Most cited

BioinformaticsbioRxivPreprintApr 7, 2026

Estimation of metabolite levels in cheese from microbial gene expression

Anis Mansouri, Rina Mekuli, Dominique Swennen…·Daniel Remondini

The paper estimates volatile metabolite levels in cheese using metatranscriptomic data and machine learning to link gene expression to biochemical pathways.

DOI 10.64898/2026.04.04.716484

metagenomicsmachine learning (classical)

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GenomicsScientific ReportsPeer-reviewedJul 1, 2025

Language models learn to represent antigenic properties of human influenza A(H3) virus

Francesco Durazzi, Marion Koopmans, Ron A. M. Fouchier, Daniel Remondini

The paper uses machine learning to predict antigenic properties of influenza A(H3) virus from genomic data, finding that deep learning models outperform classical methods in fine-grained antigenic analysis.

6 citations

humanprotein sequence

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