Alex Graudenzi, PhD

Alex Graudenzi, PhD

Tenure-track Researcher and Co-head of the DCB lab

  • Tenure-track Asst Prof (“RTD-B”), Dept. of Informatics, Systems and Communications (DISCo), Univ. of Milan-Bicocca.
  • Co-head of the Data and Computational Biology Lab, Univ. of Milan-Bicocca
  • Director of the Lake Como School on Cancer Evolution (CSCE).
  • Member of the B4 Bicocca Bioinformatics, Biostatistics and Bioimaging Centre, Milan, Italy
  • Fellow of the Institute of Bioimaging and Molecular Physiology, National Research Council (IBFM-CNR) (on leave)

TRACK RECORD (update Feb 24)

  • 78 indexed publications (Scopus), 45 of which as first, last or corresponding author 
  • Involved in 10+ funded national and international projects 
  • Patents: co-author of US Patent US11205519B2 
  • Invited speaker at 6 international conferences/seminars
  • Peer-reviewed contributions at 15+ international conferences
  • Member of the program and organizing committees of 25+ international conferences (1 as chair).
  • Member of the editorial board of 3 international journals: Cancer Informatics, Frontiers in Genetics (Computational Genomics) and Frontiers in Applied Mathematics and Statistics (Mathematical Biology)
  • Visiting scientist at New York University (US), New York Genome Center (US), University of Toronto (Canada), Catalan Institute of Oncology (Spain), Universidade NOVA de Lisboa (Portugal), Weill Cornell Medicine (US). 
  • Peer reviewer for 15+ international journals (
  • Co-developer of 15+ software tools for bioinformatics and computational biology:
  • Supervisor of 5 (current or past) PhD students in Computer Science and 3 PostDoc.
  • Computational methods for cancer and viral evolution
  • Data science for health and medicine
  • Multiscale modeling and simulation of biological systems
  • Bioinformatics methods for multi-omics (single-cell) data analysis and integration
  • Explainable AI and deep learning
  • 2021 – 2025: AIRC Investigator grant (IG): “ANAKIN” Advanced Nanotechnology to Assist Keeping the tumor microenvironment Involved in cancer Neutralization (PI: Miriam Colombo – Univ. of Milan-Bicocca). Role: collaborator (0.5 man/year 2024-2025).
  • 2019 – 2024: My First AIRC Grant: “Integrating pharmacogenomic and pharmacometabolomic data towards personalized opioid therapies for cancer pain” (PI: Francesca Colombo – ITB-CNR). Role: collaborator.
  • 2019 – 2022: Call HUB Resarch and Innovation, Regione Lombardia, POR FESR 2014-2020 project “INTERSLA” Innovation, new technological models and networks to treat ALS (PI: Giuseppe Lauria Pinter – IRCSS Istituto Neurologico “Carlo Besta”). Role: co-responsible of WP4: Bioinformatics techniques for the analysis and integration of clinical big data (1140 hours).
  • 2018 – 2023: CRUK/AIRC/AECC Accelerator Award “SCCEIC” Single-cell cancer evolution in the clinic (PI: Giovanni Tonon – IRCCS Ospedale San Raffaele.). Role: co-responsible of Programme 3: Bioinformatics analysis, single-cell data integration and evolutionary methods.
  • 2016 –: ESFRI/MIUR project SysBioNet (Director: Lilia Alberghina – Univ. of Milan-Bicocca) Role: collaborator.
  1. Fontana, D., Crespiatico, I., …, Graudenzi, A., Mologni, L., Ramazzotti, D. (2023) Evolutionary signatures of human cancers revealed via genomic analysis of over 35,000 patients. NATURE COMMUNICATIONS 14, art. no. 5982, 
  2. Patruno L, Milite S, Bergamin R, …, Graudenzi, A., Caravagna, G. (2023) A Bayesian method to infer copy number clones from single-cell RNA and ATAC sequencing. PLOS COMPUTATIONAL BIOLOGY 19(11): e1011557.
  3. Craighero, F., Angaroni, F., Stella, F., Damiani, C., Antoniotti, M., Graudenzi, A. (2023) Unity is strength: Improving the detection of adversarial examples with ensemble approaches. NEUROCOMPUTING Volume 554, 14 October 2023, 126576.
  4. Ramazzotti, D., Angaroni, F., Maspero, D., Ascolani, G., Castiglioni, I., Piazza, R., Antoniotti, M., Graudenzi, A. (2022) Variant calling from scRNA-seq data allows the assessment of cellular identity in patient-derived cell lines. NATURE COMMUNICATIONS, 13 (1), art. no. 2718. doi: 10.1038/s41467-022-30230-w
  5. Angaroni, F., Guidi, A., Ascolani, G., d’Onofrio, A., Antoniotti, M., Graudenzi, A. (2022) J-SPACE: a Julia package for the simulation of spatial models of cancer evolution and of sequencing experiments. BMC BIOINFORMATICS, 23 (1), art. no. 269. doi: 10.1186/s12859-022-04779-8
  6. Mella, L., Lal, A., Angaroni, F., Maspero, D., Piazza, R., Sidow, A., Antoniotti, M., Graudenzi, A., Ramazzotti, D. (2022) SparseSignatures: An R package using LASSO-regularized non-negative matrix factorization to identify mutational signatures from human tumor samples. STAR PROTOCOLS, 3 (3), art. no. 101513. doi: 10.1016/j.xpro.2022.101513
  7. Ramazzotti, D., Maspero, D., Angaroni, F., Spinelli, S., Antoniotti, M., Piazza, R., Graudenzi, A. (2022) Early detection and improved genomic surveillance of SARS-CoV-2 variants from deep sequencing data. ISCIENCE, 25 (6), art. no. 104487. doi: 10.1016/j.isci.2022.104487
  8. Angaroni F, Chen K, Damiani C, Caravagna G, Graudenzi A, Ramazzotti D (2022). PMCE: efficient inference of expressive models of cancer evolution with high prognostic power. BIOINFORMATICS, vol. 38, p. 754-762, ISSN: 1367-4803, doi: 10.1093/bioinformatics/btab717
  9. Ramazzotti, Daniele, Angaroni, Fabrizio, Maspero, Davide, Ascolani, Gianluca, Castiglioni, Isabella, Piazza, Rocco, Antoniotti, Marco, Graudenzi, Alex (2022). LACE: Inference of cancer evolution models from longitudinal single-cell sequencing data. JOURNAL OF COMPUTATIONAL SCIENCE, vol. 58, 101523, ISSN: 1877-7503, doi: 10.1016/j.jocs.2021.101523
  10. Graudenzi A., Maspero D., Angaroni F., Piazza R., Ramazzotti D. (2021). Mutational signatures and heterogeneous host response revealed via large-scale characterization of SARS-CoV-2 genomic diversity. ISCIENCE, vol. 24, 102116, ISSN: 2589-0042, doi: 10.1016/j.isci.2021.102116
  11. Patruno, L, Maspero, D, Craighero, F, Angaroni, F, Antoniotti, M, Graudenzi, A (2021). A review of computational strategies for denoising and imputation of single-cell transcriptomic data. BRIEFINGS IN BIOINFORMATICS, vol. 22, bbaa222, ISSN: 1467-5463, doi: 10.1093/bib/bbaa222
  12. Ramazzotti. , Daniele, Angaroni, Fabrizio, Maspero, Davide, Gambacorti-Passerini, Carlo, Antoniotti, Marco, Graudenzi, Alex, Piazza, Rocco (2021). VERSO: a comprehensive framework for the inference of robust phylogenies and the quantification of intra-host genomic diversity of viral samples. PATTERNS, vol. 2, 100212, ISSN: 2666-3899, doi: 10.1016/j.patter.2021.100212
  13. Damiani, C, Maspero, D, Di Filippo, M, Colombo, R, Pescini, D, Graudenzi, A, Westerhoff HV, Alberghina, L, Vanoni, M, Mauri, G (2019). Integration of single-cell RNA-seq data into population models to characterize cancer metabolism. PLOS COMPUTATIONAL BIOLOGY, vol. 15, e1006733, ISSN: 1553-7358, doi: 10.1371/journal.pcbi.1006733
  14. Graudenzi, A, Maspero,D, Di Filippo,M, Gnugnoli,M, Isella,C, 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, vol. 87, p. 37-49, ISSN: 1532-0464, doi: 10.1016/j.jbi.2018.09.010
  15. Caravagna, G, GRAUDENZI, ALEX, Ramazzotti, D, Sanz Pamplona, R, De Sano, L, MAURI, GIANCARLO, Moreno, V, ANTONIOTTI, MARCO, Mishra, B. (2016). Algorithmic methods to infer the evolutionary trajectories in cancer progression. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, vol. 113, p. E4025-E4034, ISSN: 1091-6490, doi: 10.1073/pnas.1520213113
  16. RAMAZZOTTI, DANIELE, CARAVAGNA, GIULIO, Olde Loohuis, L, GRAUDENZI, ALEX, Korsunsky, I, MAURI, GIANCARLO, ANTONIOTTI, MARCO, Mishra, B. (2015). CAPRI: Efficient Inference of Cancer Progression Models from Cross-sectional Data. BIOINFORMATICS, vol. 31, p. 3016-3026, ISSN: 1367-4803, doi: 10.1093/bioinformatics/btv296
  17. GRAUDENZI, ALEX, CARAVAGNA, GIULIO, De Matteis, G, ANTONIOTTI, MARCO (2014). Investigating the relation between stochastic differentiation and homeostasis in intestinal crypts via multiscale modeling. PLOS ONE, vol. 9, e97272, ISSN: 1932-6203, doi: 10.1371/journal.pone.0097272
  18. De Matteis, G, GRAUDENZI, ALEX, ANTONIOTTI, MARCO (2013). A review of spatial computational models for multi-cellular systems, with regard to intestinal crypts and colorectal cancer development. JOURNAL OF MATHEMATICAL BIOLOGY, vol. 66, p. 1409-1462, ISSN: 0303-6812, doi: 10.1007/s00285-012-0539-4
  19. Graudenzi A, Serra R, Villani M, Damiani C, Colacci A, Kauffman S (2011). Dynamical properties of a Boolean model of gene regulatory network with memory. JOURNAL OF COMPUTATIONAL BIOLOGY, vol. 18, p. 1291-1303, ISSN: 1066-5277, doi: 10.1089/cmb.2010.0069
  20. Serra, R, Villani, M, Graudenzi, A, Kauffman, SA (2007). Why a simple model of genetic regulatory networks describes the distribution of avalanches in gene expression data. JOURNAL OF THEORETICAL BIOLOGY, vol. 246, p. 449-460, ISSN: 0022-5193, doi: 10.1016/j.jtbi.2007.01.012

Address:  Viale Sarca 336, Milan – U14 Building, room 2022 (2nd floor)