Author of 100+ publications on indexed Journals and Conference Proceedings.
H-index: 18 (source Google Scholar)
1000+ citations
Research keywords: data science, artificial intelligence, bioinformatics, computational biology, complex systems, cancer/viral evolution.
Selected publications
Graudenzi, A.*, Maspero, D., Angaroni, F., Piazza, R., Ramazzotti, D. (* lead contact) (2021). Mutational Signatures and Heterogeneous Host Response Revealed Via Large-Scale Characterization of SARS-CoV-2 Genomic Diversity. iScience 24, 102116, doi: 10.1016/j.isci.2021.102116
Ramazzotti, D., Angaroni, F., Maspero, D., Gambacorti-Passerini, C., Antoniotti, M., Graudenzi, A.*, Piazza, R. (* lead contact) (2021). VERSO: a comprehensive framework for the inference of robust phylogenies and the quantification of intra-host genomic diversity of viral samples. Patterns 2, 100212, doi: 10.1016/j.patter.2021.100212
Patruno, L., Maspero, D., Craighero, F., Angaroni, F., Antoniotti, M., Graudenzi, A.*: A Review of Computational Strategies for Denoising and Imputation of Single-cell Transcriptomic Data (* corresponding author) (2020). Briefings in Bioinformatics (in press), doi: 10.1093/bib/bbaa222
Angaroni, F., Graudenzi, A.*, Rossignolo, M., Maspero, D., Calarco, T., Piazza, R., Montangero. S., Antoniotti, M.: An optimal control framework for the automated design of personalized cancer treatments (* corresponding author) (2020). Front. Bioeng. Biotechnol 8:523, doi: 10.3389/fbioe.2020.00523.
Damiani, C., Maspero, D., Di Filippo, M., Colombo, R., Pescini, D., Graudenzi, A., Westerhoff, H. V., Alberghina, L., Vanoni, M., Mauri, G. (2019): Integration of single-cell RNA-seq data into population models to characterize cancer metabolism. Plos Computational Biology 15(2): e1006733, https://doi.org/10.1371/journal.pcbi.1006733.
Ramazzotti, D., Graudenzi, A.*, De Sano, L., Antoniotti, M., Caravagna, G. (* corresponding author) (2019): Learning mutational graphs of individual tumor evolution from multi-sample sequencing data. BMC Bioinformatics, 20:210, doi: 10.1186/s12859-019-2795-4
Graudenzi, A., Maspero, D., Isella, C., Di Filippo, M., Mauri, G., Medico, E., Antoniotti, M., Damiani, C. (2018): Integration of transcriptomic data and metabolic networks in cancer samples reveals highly significant prognostic power. Journal of Biomedical Informatics, Volume 87, 37-49, doi: 10.1016/j.jbi.2018.09.010
Caravagna, G., Graudenzi, A., Ramazzotti, D., Sanz-Pamplona, R., De Sano, L., Mauri, G., Moreno, V., Antoniotti, M., and Mishra, B. (2016): Algorithmic Methods to Infer the Evolutionary Trajectories in Cancer Progression. Proc Natl Acad Sci (PNAS) USA 113 (28) E4025-E4034; published ahead of print June 28, 2016, doi:10.1073/pnas.1520213113
Ramazzotti, D., Caravagna, G., Olde Loohuis, L., Graudenzi, A., Korsunsky, I., Mauri, G., Antoniotti, M., Mishra, B. (2015): CAPRI: Efficient Inference of Cancer Progression Models from Cross-sectional Data. Bioinformatics 31 (18): 3016-3026, doi: 10.1093/bioinformatics/btv296.
Graudenzi, A.*, Caravagna, G.*, De Matteis, G., Antoniotti, M. (2014) Investigating the Relation between Stochastic Differentiation, Homeostasis and Clonal Expansion in Intestinal Crypts via Multiscale Modeling (* equal contributors). PLoS ONE 9(5): e97272. doi:10.1371/journal.pone.0097272.
Filisetti, A., Graudenzi, A., Serra, R., Villani, M., Fuchslin, R. M., Packard, N., Kauffman, S. A., Poli, I., (2012): A stochastic model of autocatalytic reaction networks. Theory in Biosciences, 131(2): 85-93, doi: 10.1007/s12064-011-0136-x.
Graudenzi, A., Serra, R., Villani, M., Damiani, C., Colacci, A., Kauffman, S.A., (2011): Dynamical properties of a Boolean model of gene regulatory network with memory. Journal of Computational Biology, Vol. 18, n.10: 1291-1303. doi: 10.1089/cmb.2010.0069.
Serra, R., Villani. M., Graudenzi, A., Kauffman, S.A., (2007): Why a simple model of genetic regulatory networks describes the distribution of avalanches in gene expression data. Journal of Theoretical Biology 249: 449-460, doi: 10.1016/j.jtbi.2007.01.012
Laboratorio Data and Computational Biology
