Research themes

• • omics data analysis and integration (transcriptomics, proteomics, metabolomics, and mirnomics)
  • design and implementation of computational strategies for constraint-based modeling for metabolic flux simulation.
  • design and implementation of Machine Learning models for biotechnologies.

CV

Educational experience

I have a strong teaching experience. During my postdoc, I was teaching assistant, for the Operational Research course at the Computer Science department in University of Milano-Bicocca from 2019 to 2022.

Moreover, I am teaching Data Science  at the Biotechnology department in the University of Insubria from 2023

I was also Co-Supervisor thesis of several Bachelor and Master students.

Collaborations

• Data and Computational Biology (DCB), Department of Informatics, Systems, and Communication, University of Milan – Bicocca.
• Department of Biotechnology and Biosciences, University of Milan- Bicocca
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Affiliations

• InfoLife Laboratory – CINI. a group of Computational researchers working on bioinformatics dissemination and challenges.
• SYSBIO – Centre of Systems Biology. SYSBIO is a Research Centre and a Research Infrastructure (RI), the Italian Candidate Node for ISBE, and the ESFRI project for systems biology.
• BITS – Bioinformatics Italian Society. The main goals of the association are the study, development and spreading of Bioinformatics in a scientific, academic, technological and industrial environment.

Selected publications

1. Giammona, A., Galuzzi, B. G., Imperia, E., Gervasoni, C., Remedia, S., Restaneo, L., … & Bertoli, G. (2025). Chronic Gastrointestinal Disorders and miRNA-Associated Disease: An Up-to-Date. International Journal of Molecular Sciences, 26(1), 413.
2. Galuzzi, B. G., Milazzo, L., & Damiani, C. (2024). Adjusting for false discoveries in constraint-based differential metabolic flux analysis. Journal of Biomedical Informatics, 150, 104597.
3. Bonetti, M., Franchini, A., Galuzzi, B. G., & Sesana, A. (2024). Neural networks unveiling the properties of gravitational wave background from supermassive black hole binaries. Astronomy & Astrophysics, 687, A42.
4. Di Filippo, M., Pescini, D., Galuzzi, B. G., Bonanomi, M., Gaglio, D., Mangano, E., … & Damiani, C. (2022). INTEGRATE: Model-based multi-omics data integration to characterize multi-level metabolic regulation. PLoS Computational Biology, 18(2), e1009337.
5. Galuzzi, B. G., Vanoni, M., & Damiani, C. (2022). Combining denoising of RNA-seq data and flux balance analysis for cluster analysis of single cells. BMC bioinformatics, 23(6), 1-21.
6. Galuzzi, B. G., Mirarchi, A., Viganò, E. L., De Gioia, L., Damiani, C., & Arrigoni, F. (2022). Machine learning for efficient prediction of protein redox potential: The flavoproteins case. Journal of Chemical Information and Modeling, 62(19), 4748-4759.
7. Galuzzi, B. G., Giordani, I., Candelieri, A., Perego, R., & Archetti, F. (2020). Hyperparameter optimization for recommender systems through Bayesian optimization. Computational Management Science, 17, 495-515.
8. Candelieri, A., Galuzzi, B. G., Giordani, I., & Archetti, F. (2019). Vulnerability of public transportation networks against directed attacks and cascading failures. Public Transport, 11, 27-49.