Publications

LIST OF PUBBLICATIONS

Pubblications with * show authors in alphabetical order.

Publications where I am co-first or co-last author are marked with †.

The copyright of each publication is held by the corresponding publisher, PDFs are provided for personal use only

Journals:

1. Integrative analysis of KEAP1/NFE2L2 alterations across 3600+ tumors reveals an NRF2 expression signature as a prognostic biomarker in cancer. Crippa V., Cordani N., Villa A.M., Malighetti F., Villa M., et al. npj Precision Oncology, 2025; doi:10.1038/s41698-025-01088-0.

2. Comprehensive analysis of mutational processes across 20,000 adult and pediatric tumors. Villa M., Malighetti F., De Sano L., Villa A.M., Cordani N., et al. Nucleic Acids Research, 2025; doi:10.1093/nar/gkaf648.

3. Integrative multi-omics analysis enables a comprehensive characterization of prostate cancer and unveils metastasis-associated candidate biomarkers†. Villa M., Cazzaniga G., Bolognesi M, Malighetti F., Crippa V., et al. Heliyon, 2025; doi:10.1016/j.heliyon.2025.e43533.

4. Protocol for obtaining cancer type and subtype predictions using subSCOPE. Grewal J.K., Robertson A.G., Ellrott K., Wong C.K., Lee J.A., et al. STAR Protocols, 2025; doi:10.1016/j.xpro.2025.103705.

5. Protocol for assessing distances in pathway space for classifier feature sets from machine learning methods. Tercan B., Apolonio V.H., Chagas V.S., Wong C.K., Lee J.A., et al. STAR Protocols, 2025; doi:10.1016/j.xpro.2025.103681.

6. Prognostic Biomarkers in Breast Cancer via Multi-Omics Clustering Analysis. Malighetti F., Villa M., Villa A.M., Pelucchi S., Aroldi A., et al. International Journal of Molecular Sciences, 2025; doi:10.3390/ijms26051943.

7. Classification of non-TCGA cancer samples to TCGA molecular subtypes using compact feature sets. K. Ellrott, C. K. Wong, C. Yau, M. A.A. Castro, J. A. Lee, et al. Cancer Cell, 2025; doi:10.1016/j.ccell.2024.12.002.

8. Hb Monza: A novel extensive HBB duplication with preserved α-β subunit interaction and unstable hemoglobin phenotype. I. Civettini, A. Zappaterra, P. Corti, A. Messina, A. Aroldi, et al. Med, 2024; doi:10.1016/j.medj.2024.11.007.

9. Anaplastic Lymphoma Kinase (ALK) Inhibitors Enhance Phagocytosis Induced by CD47 Blockade in Sensitive and Resistant ALK-Driven Malignancies. F. Malighetti, M. Villa, M. Mauri, S. Piane, V. Crippa, et al. Biomedicines, 2024; doi:10.3390/biomedicines12122819.

10. Recurrent somatic mutations of FAT family cadherins induce an aggressive phenotype and poor prognosis in anaplastic large cell lymphoma. M. Villa, G.G. Sharma, F. Malighetti, M. Mauri, G. Arosio, et al. British Journal of Cancer, 2024; doi:10.1038/s41416-024-02881-7.

11. Tumor evolution metrics predict recurrence beyond 10 years in locally advanced prostate cancer. J. Fernandez-Mateos, G.D. Cresswell, N. Trahearn, K. Webb, C. Sakr, et al. Nature Cancer, 2024; doi:10.1038/s43018-024-00787-0.

12. Clonal Lineage Tracing with Somatic Delivery of Recordable Barcodes Reveals Migration Histories of Metastatic Prostate Cancer. R.N. Serio, A. Scheben, B. Lu, D.V. Gargiulo, L. Patruno, et al. Cancer Discovery, 2024; doi:10.1158/2159-8290.CD-23-1332.

13. Control-FREEC viewer: a tool for the visualization and exploration of copy number variation data. V. Crippa, E. Fina, D. Ramazzotti, R. Piazza. BMC Bioinformatics, 2024; doi:10.1186/s12859-024-05694-w.

14. Differential Expression of NOTCH-1 and Its Molecular Targets in Response to Metronomic Followed by Conventional Therapy in a Patient with Advanced Triple-Negative Breast Cancer. A. Ilari, V. Cogliati, N. Sherif, E. Grassilli, D. Ramazzotti, et al. Biomedicines, 2024; doi:10.3390/biomedicines12020272.

15. Idiopathic erythrocytosis: a germline disease? E. M. Elli, M. Mauri, D. D’Aliberti, I. Crespiatico, D. Fontana, et al. Clinical and Experimental Medicine, 2024; doi:10.1007/s10238-023-01283-y.

16. First-hit SETBP1 mutations cause a myeloproliferative disorder with bone marrow fibrosis. I. Crespiatico, M. Zaghi, C. Mastini, D. D’Aliberti, M. Mauri, et al. Blood, 2024; doi:10.1182/blood.2023021349.

17. Evaluating the performance of large language models in haematopoietic stem cell transplantation decision-making†. I. Civettini, A. Zappaterra, B. M. Granelli, G. Rindone, A. Aroldi, et al. British Journal of Haematology, 2023; doi:10.1111/bjh.19200.

18. Contribution of pks+ E. coli mutations to colorectal carcinogenesis†. B. Chen, D. Ramazzotti, T. Heide, I. Spiteri, J. Fernandez-Mateos, et al. Nature Communications, 2023; doi:10.1038/s41467-023-43329-5. Media. Media. Media. Media.

19. Evolutionary signatures of human cancers revealed via genomic analysis of over 35,000 patients. D. Fontana, I. Crespiatico, V. Crippa, F. Malighetti, M. Villa, et al. Nature Communications, 2023; doi:10.1038/s41467-023-41670-3. Media. Media. Media.

20. Effects of blocking CD24 and CD47 ‘don’t eat me’ signals in combination with rituximab in mantle-cell lymphoma and chronic lymphocytic leukaemia. A. Aroldi, M. Mauri, D. Ramazzotti, M. Villa, F. Malighetti, et al. Journal of Cellular and Molecular Medicine, 2023; doi:10.1111/jcmm.17868.

21. Characterization of cancer subtypes associated with clinical outcomes by multi-omics integrative clustering. V. Crippa, F. Malighetti, M. Villa, A. Graudenzi, R. Piazza, et al. Computers in Biology and Medicine, 2023; doi:10.1016/j.compbiomed.2023.107064.

22. LACE 2.0: an interactive R tool for the inference and visualization of longitudinal cancer evolution. G. Ascolani, F. Angaroni, D. Maspero, F. Craighero, N. L. S. Bhavesh, et al. BMC Bioinformatics, 2023; doi:10.1186/s12859-023-05221-3.

23. DNA Damage Response (DDR) Is Associated With Treatment-free Remission in Chronic Myeloid Leukemia Patients. F. Malighetti, G. Arosio, C. Manfroni, M. Mauri, M. Villa, et al. HemaSphere, 2023; doi:10.1097/HS9.0000000000000852.

24. Targeting the immune microenvironment in Waldenstr ̈om Macroglobulinemia via halting the CD40/CD40- ligand axis. A. Sacco, V. Desantis, J. Celay, V. Giustini, F. Rigali, et al. Blood, 2023; doi:10.1182/blood.2022019240.

25. Characterization of SARS-CoV-2 Mutational Signatures from 1.5+ Million Raw Sequencing Samples. A. Aroldi, F. Angaroni, D. D’Aliberti, S. Spinelli, I. Crespiatico, et al. Viruses, 2022; doi:10.3390/v15010007.

26. Pan-cancer landscape of AID-related mutations, composite mutations, and their potential role in the ICI response. I. Hernandez-Verdin, K.C. Akdemir, D. Ramazzotti, G. Caravagna, K. Labreche, et al. npj Precision Oncology, 2022; doi:10.1038/s41698-022-00331-2.

27. Phenotypic plasticity and genetic control in colorectal cancer evolution. J. Househam, T. Heide, G. D. Cresswell, I. Spiteri, C. Kimberley, et al. Nature, 2022; doi:10.1038/s41586-022-05311-x.

28. The co-evolution of the genome and epigenome in colorectal cancer. T. Heide,  J. Househam,  G. D. Cresswell, I. Spiteri, C. Lynn, et al. Nature, 2022; doi:10.1038/s41586-022-05202-1.

29. SparseSignatures: An R package using LASSO-regularized non-negative matrix factorization to identify mutational signatures from human tumor samples. L. Mella, A. Lal, F. Angaroni, D. Maspero, R. Piazza, et al. STAR Protocols, 2022; doi:10.1016/j.xpro.2022.101513.

30. Early detection and improved genomic surveillance of SARS-CoV-2 variants from deep sequencing data. D. Ramazzotti, D. Maspero, F. Angaroni, S. Spinelli, M. Antoniotti, et al. iScience, 2022; doi:10.1016/j.isci.2022.104487. Media. Media. Media. Media.

31. Variant calling from scRNA-seq data allows the assessment of cellular identity in patient-derived cell lines. D. Ramazzotti, F. Angaroni, D. Maspero, G. Ascolani, I. Castiglioni, et al. Nature Communications, 2022; doi:10.1038/s41467-022-30230-w.

32. Large-scale analysis of SARS-CoV-2 synonymous mutations reveals the adaptation to the human codon usage during the virus evolution†. D. Ramazzotti, F. Angaroni, D. Maspero, M. Mauri, D. D’Aliberti, et al. Virus Evolution, 2022; doi:10.1093/ve/veac026. Media.

33. LACE: Inference of cancer evolution models from longitudinal single-cell sequencing data†. D. Ramazzotti, F. Angaroni, D. Maspero, G. Ascolani, I. Castiglioni, et al. Journal of Computational Science, 2022; doi:10.1016/j.jocs.2021.101523.

34. VirMutSig: Discovery and assignment of viral mutational signatures from sequencing data. D. Maspero, F. Angaroni, D. Porro, R. Piazza, A. Graudenzi, et al. STAR Protocols, 2021; doi:10.1016/j.xpro.2021.100911.

35. PMCE: efficient inference of expressive models of cancer evolution with high prognostic power. F. Angaroni, K. Chen, C. Damiani, G. Caravagna, A. Graudenzi, D. Ramazzotti. Bioinformatics, 2021; doi:10.1093/bioinformatics/btab717.

36. Investigating the performance of multi-objective optimization when learning Bayesian Networks*. P. Cazzaniga, M. S. Nobile, D. Ramazzotti. Neurocomputing, 2021; doi:10.1016/j.neucom.2021.07.049.

37. De Novo Mutational Signature Discovery in Tumor Genomes using SparseSignatures. A. Lal, K. Liu, R. Tibshirani, A. Sidow, D. Ramazzotti. PLOS Computational Biology, 2021; doi:10.1371/journal.pcbi.1009119.

38. Learning the structure of Bayesian Networks via the bootstrap†. G. Caravagna and D. Ramazzotti. Neurocomputing, 2021; doi:10.1016/j.neucom.2021.03.071.

39. Mutational Signatures and Heterogeneous Host Response Revealed Via Large-Scale Characterization of SARS-COV-2 Genomic Diversity†. A. Graudenzi, D. Maspero, F. Angaroni, R. Piazza, D. Ramazzotti. iScience, 2021; doi:10.1016/j.isci.2021.102116. Media. Media. Media.

40. VERSO: a comprehensive framework for the inference of robust phylogenies and the quantification of intra-host genomic diversity of viral samples. D. Ramazzotti, F. Angaroni, D. Maspero, C. Gambacorti-Passerini, M. Antoniotti, et al. Patterns, 2021; doi:10.1016/j.patter.2021.100212. Media. Media. Media.

41. Integrated Genomic, Functional, and Prognostic Characterization of Atypical Chronic Myeloid Leukemia. D. Fontana, D. Ramazzotti, A. Aroldi, S. Redaelli, V. Magistroni, et al. HemaSphere, 2020; doi:10.1097/HS9.0000000000000497.

42. The Influence of Nutrients Diffusion on a Metabolism-driven Model of a Multi-cellular System. D. Maspero, C. Damiani, M. Antoniotti, A. Graudenzi, M. Di Filippo, et al. Fundamenta Informaticae, 2020; doi:10.3233/FI-2020-1883 .

43. Machine learning can accurately predict pre-admission baseline hemoglobin and creatinine in intensive care patients. A. Dauvin, C. Donado, P. Bachtiger, K. C. Huang, C. Sauer, et al. npj Digital Medicine, 2019; doi:10.1038/s41746-019-0192-z .

44. Assessment of network module identification across complex diseases. S. Choobdar, M. E. Ahsen, J. Crawford, M. Tomasoni, T. Fang et al. Nature Methods, 2019; doi:10.1038/s41592-019-0509-5 .

45. Comprehensive genomic characterization of breast tumors with BRCA1 and BRCA2 mutations†. A. Lal, D. Ramazzotti, Z. Weng, K. Liu, J. M. Ford, et al. BMC Medical Genomics, 2019; doi:10.1186/s12920-019-0545-0. 

46. Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data. D. Ramazzotti, A. Graudenzi, L. De Sano, M. Antoniotti, G. Caravagna. BMC Bioinformatics, 2019; doi:10.1186/s12859-019-2795-4 .

47. Withholding or withdrawing invasive interventions may not accelerate time to death among dying ICU patients. D. Ramazzotti, P. Clardy, L. A. Celi, D. J. Stone, R. S. Rudin. PLoS ONE 2019 14(2): e0212439; doi:10.1371/journal.pone.0212439. Media. 

48. Efficient computational strategies to learn the structure of probabilistic graphical models of cumulative phenomena. D. Ramazzotti, M. S. Nobile, M. Antoniotti, A. Graudenzi. Journal of Computational Science, 2018; doi:10.1016/j.jocs.2018.10.009.

49. Improved survival of cancer patients admitted to the ICU between 2002 and 2011 at a U.S. teaching hospital. C. Sauer, J. Dong, L. A. Celi, D. Ramazzotti. Cancer Research and Treatment, 2018; doi:10.4143/crt.2018.360.

50. Multi-omic tumor data reveal diversity of molecular mechanisms that correlate with survival†. D. Ramazzotti, A. Lal, B. Wang, S. Batzoglou, A. Sidow. Nature Communications, 2018; doi:10.1038/s41467-018-06921-8. Media. 

51. Learning the structure of Bayesian Networks: A quantitative assessment of the effect of different algorithmic schemes*. S. Beretta, M. Castelli, I. Goncalves, R. Henriques, D. Ramazzotti. Complexity, 2018; doi:10.1155/2018/1591878.

52. Detecting repeated cancer evolution from multi-region tumor sequencing data. G. Caravagna, Y., D. Ramazzotti, T. A. Graham, G. Sanguinetti, et al. Nature Methods, 2018; doi:10.1038/s41592-018-0108-x . Media. 

53. Modeling cumulative biological phenomena with Suppes-Bayes causal networks. D. Ramazzotti, A. Graudenzi, G. Caravagna, M. Antoniotti. Evolutionary Bioinformatics, 2018; doi:10.1177/1176934318785167Evolutionary Bioinformatics, 2018; doi:10.1177/1176934318785167.

54. Causal Data Science for Financial Stress Testing*. G. Gao, B. Mishra. D. Ramazzotti. Journal of Computational Science, 2018; doi:10.1016/j.jocs.2018.04.003.

55. SIMLR: A Tool for Large-Scale Genomic Analyses by Multi-Kernel Learning†. B. Wang, D. Ramazzotti, L. De Sano, J. Zhu, E. Pierson, et al. Proteomics, 2017; doi:10.1002/pmic.201700232.

56. OncoScore: a novel, Internet-based tool to assess the oncogenic potential of genes. R. Piazza, D. Ramazzotti, R. Spinelli, A. Pirola, L. De Sano, et al. Scientific Reports, 2017; doi:10.1038/srep46290.

57. Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning. B. Wang, J. Zhu, E. Pierson, D. Ramazzotti, S. Batzoglou. Nature Methods, 2017; doi:10.1038/nmeth.4207.

58. Exposing the Probabilistic Causal Structure of Discrimination*. F. Bonchi, S. Hajian, B. Mishra, D. Ramazzotti. International Journal of Data Science and Analytics, 2017; doi:10.1007/s41060-016-0040-z3.

59. Design of the TRONCO BioConductor Package for TRanslational ONCOlogy*. M. Antoniotti, G. Caravagna, L. De Sano, A. Graudenzi, G. Mauri, et al. The R Journal. 8 (2): 74-78, 2016; doi:10.32614/rj-2016-032.

60. Algorithmic Methods to Infer the Evolutionary Trajectories in Cancer Progression. G. Caravagna, A. Graudenzi, D. Ramazzotti, R. Sanz-Pamplona, L. De Sano, et al. Proceedings of the National Academy of Sciences 2016 113 (28) E4025-E4034; doi:10.1073/pnas.1520213113. Media, Media.

61. TRONCO: an R package for the inference of cancer progression models from heterogeneous genomic data†. L. De Sano, G. Caravagna, D. Ramazzotti, A. Graudenzi, G. Mauri, et al. Bioinformatics 2016; doi:10.1093/bioinformatics/btw035.

62. CAPRI: Efficient Inference of Cancer Progression Models from Cross-sectional Data. D. Ramazzotti, G. Caravagna, L. Olde-Loohuis, A. Graudenzi, I. Korsunsky, et al. Bioinformatics 2015; doi:10.1093/bioinformatics/btv296.

63. Inferring tree causal models of cancer progression with probability raising†. L. Olde-Loohuis, G. Caravagna, A. Graudenzi, D. Ramazzotti, G. Mauri, et al. PLoS ONE 2014 9(10): e108358; doi:10.1371/journal.pone.0108358.

Books:

1. Data Pre-processing*. B. Malley, D. Ramazzotti, J. T. Wu. Chapter in: Secondary Analysis of Electronic Health Records. Authors: MIT Critical Data. Springer International Publishing, 2016. ISBN 978-3-319-43742-2; doi:10.1007/978-3-319-43742-2.

Proceedings:

1. Exploring the Solution Space of Cancer Evolution Inference Frameworks for Single-Cell Sequencing Data. D. Maspero, F. Angaroni, L. Patruno, D. Ramazzotti, D. Posada, A. Graudenzi. Proceedings of the Italian Workshop on Artificial Life and Evolutionary Computation (WIVACE 2022), Communications in Computer and Information Science pp 70–81, 2023; doi:10.1007/978-3-031-31183- 3 61.

2. cyTRON and cyTRON/JS: two Cytoscape-based applications for the inference of cancer evolution models. L. Patruno, E. Galimberti, D. Ramazzotti, G. Caravagna, L. De Sano, et al. Computational Intelligence methods for Bioinformatics and Biostatistics (CIBB), 2019; doi:10.1007/978-3-030-63061-4_2.

3. Probabilistic Causal Analysis of Social Influence*. F. Bonchi, F. Gullo, B. Mishra, D. Ramazzotti. Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM), 1003-1012 , 2018; doi:10.1145/3269206.3271756.

4. Combining Bayesian Approaches and Evolutionary Techniques for the Inference of Breast Cancer Networks*. S. Beretta, M. Castelli, I. Gonalves, I. Merelli, D. Ramazzotti. International Conference on Evolutionary Computation Theory and Applications, 2016; doi:10.5220/0006064102170224.

5. Parallel Implementation of Efficient Search Schemes for the Inference of Cancer Progression Models. D. Ramazzotti, M. S. Nobile, P. Cazzaniga, G. Mauri, M. Antoniotti Proceedings of IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology, Chiang Mai (Thailand), 2016; doi:10.1109/cibcb.2016.7758109.

6. A Model of Colonic Crypts using SBML Spatial. D. Ramazzotti, C. Maj and M. Antoniotti. Proceedings of the Italian Workshop on Artificial Life and Evolutionary Computation (WIVACE 2013), Electronic Proceedings in Theoretical Computer Science 130, 74-78, 2013; doi:10.4204/eptcs.130.11.

Theses:

1. A Model of Selective Advantage for the Efficient Inference of Cancer Clonal Evolution. Ph.D. Thesis, Dipartimento di Informatica, Università degli Studi di Milano-Bicocca, 2016. Advisors: M. Antoniotti, G. Mauri, B. Mishra, F. Stella.

2. An Observational Study: The Effect of Diuretics Administration on Outcomes of Mortality and Mean Duration of I.C.U. Stay. M.Sc. Thesis, Dipartimento di Informatica, Università degli Studi di Milano-Bicocca, 2012. Advisors: G. Mauri, U. M. O'Reilly, L. Vanneschi.

3. Interazioni con il Web Service di Google Maps. B.Sc. Thesis, Facoltà di Ingegneria. Politecnico di Milano, 2009. Advisor: C. Cappiello.