DISEASE-04: bioinformatics and AI experts available to join consortia

Expertise in modeling and analysis of biological and biomedical data with a “One health” approach, as developed within the EU VEO project (WP2 “Advanced datamining tools” co-leader) or in E-MUSE MSCA project on novel food development and analysis.

Application of Artificial Intelligence methods (e.g. NLP tools for protein sequence analysis): Language models learn to represent antigenic properties of human influenza A(H3) virus, Francesco Durazzi, Marion P. G. Koopmans, Ron A. M. Fouchier & Daniel Remondini, Scientific Reports | (2025) 15:21364

Application of network theory approaches for the analysis of metagenomic data (human, animal, environmental): Metagenomics data: tell me which metrics and I will tell you which communities, Alessandro Fuschi, Alessandra Merlotti, Daniel Remondini, ISME Commun. 2025 Jul 24;5(1):ycaf125.  doi: 10.1093/ismeco/ycaf125; Time-series sewage metagenomics distinguishes seasonal, human-derived and environmental microbial communities potentially allowing source-attributed surveillance. Becsei Á, Fuschi A, …, Remondini D, Aarestrup FM, Munk P. Nat Commun. 2024;15(1):7551. doi: 10.1038/s41467-024-51957-8

Application of mathematical modelling for epidemic evolution characterization  integrating multiple data types (genomic, clinical, demographic, mobility): Human mobility and sewage data correlate with COVID-19 epidemic evolution in a

3-year surveillance of the metropolitan area of Bologna, Francesco Durazzi, Enrico Lunedei, Giulio Colombini, Giulia Gatti, Vittorio Sambri, Alessandra De Cesare,Cecilia Crippa, Frédérique Pasquali, Gastone Castellani, Daniel Remondini, Armando Bazzani and Bologna MODELS4COVID Study Group, Durazzi et al. BMC Infectious Diseases (2025) 25:1467; Modelling antimicrobial resistance transmission to guide personalized antimicrobial stewardship interventions and infection control policies in healthcare setting: a pilot study, F. Durazzi, …, Daniel Remondini & Evelina Tacconell, Sci. Rep. (2023) 13:15803 https://doi.org/10.1038/s41598-023-42511-5