GALUZZI BRUNO GIOVANNI

Galuzzi, B., Izzo, S., Giampaolo, F., Cuomo, S., Vanoni, M., Alberghina, L., et al. (2023). Coupling constrained-based flux sampling and clustering to tackle cancer metabolic heterogeneity. In 2023 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) (pp.185-192). IEEE [10.1109/PDP59025.2023.00037]. Dettaglio

Galuzzi, B., Milazzo, L., Damiani, C. (2023). Best Practices in Flux Sampling of Constrained-Based Models. In Machine Learning, Optimization, and Data Science
8th International Conference, LOD 2022, Certosa di Pontignano, Italy, September 18–22, 2022, Revised Selected Papers, Part II (pp.234-248). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-031-25891-6_18]. Dettaglio

Galuzzi, B., Damiani, C. (2023). An Efficient Implementation of Flux Variability Analysis for Metabolic Networks. In Artificial Life and Evolutionary Computation - 16th Italian Workshop, WIVACE 2022, Gaeta, Italy, September 14–16, 2022, Revised Selected Papers (pp.58-69). Springer [10.1007/978-3-031-31183-3_5]. Dettaglio

Di Filippo, M., Pescini, D., Galuzzi, B., Bonanomi, M., Gaglio, D., Mangano, E., et al. (2022). INTEGRATE: Model-based multi-omics data integration to characterize multi-level metabolic regulation. PLOS COMPUTATIONAL BIOLOGY, 18(2) [10.1371/journal.pcbi.1009337]. Dettaglio

Galuzzi, B., Mirarchi, A., Viganò, E., 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 [10.1021/acs.jcim.2c00858]. Dettaglio