Translational Bioinformatics Laboratory (TBL)
Department of Medicine and Surgery · University of Milano-Bicocca

Selected work

Publications

The Translational Bioinformatics Laboratory (TBL) publishes in the fields of computational biology, cancer genomics, bioinformatics, statistics, and translational medicine.

Below you can find a selection of recent papers, followed by the full list of publications, including journal articles, conference proceedings, book chapters, and theses.

Publications with * indicate authors listed in alphabetical order. Publications where Daniele Ramazzotti is co-first or co-last author are marked with †.

For a complete and continuously updated list, please refer to Google Scholar →

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Recent selected publications

Journal articles

  1. Palbociclib targets β-catenin for degradation and synergizes with KRAS or ERK5 inhibition in colorectal cancer preclinical models
    Villa M., Malighetti F., Villa A.M., Cordani N., Aroldi A., et al.
    Journal of Translational Medicine, 2026.
  2. Prognostic Score for Myelodysplastic Syndromes Based on Molecular Evolution
    Civettini I., Malighetti F., Villa M., Crippa V., Aroldi A., et al.
    NEJM Evidence, 2026.
  3. BASCULE: bayesian inference and clustering of mutational signatures leveraging biological priors
    Buscaroli E., Sadr A., Bergamin R., Milite S., Villegas Garcia E.A., et al.
    Genome Biology, 2026.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. Classification of non-TCGA cancer samples to TCGA molecular subtypes using compact feature sets
    Ellrott K., Wong C.K., Yau C., Castro M.A.A., Lee J.A., et al.
    Cancer Cell, 2025.
  11. Hb Monza: A novel extensive HBB duplication with preserved α-β subunit interaction and unstable hemoglobin phenotype
    Civettini I., Zappaterra A., Corti P., Messina A., Aroldi A., et al.
    Med, 2024.
  12. Anaplastic Lymphoma Kinase (ALK) Inhibitors Enhance Phagocytosis Induced by CD47 Blockade in Sensitive and Resistant ALK-Driven Malignancies
    Malighetti F., Villa M., Mauri M., Piane S., Crippa V., et al.
    Biomedicines, 2024.
  13. Recurrent somatic mutations of FAT family cadherins induce an aggressive phenotype and poor prognosis in anaplastic large cell lymphoma
    Villa M., Sharma G.G., Malighetti F., Mauri M., Arosio G., et al.
    British Journal of Cancer, 2024.
  14. Tumor evolution metrics predict recurrence beyond 10 years in locally advanced prostate cancer
    Fernandez-Mateos J., Cresswell G.D., Trahearn N., Webb K., Sakr C., et al.
    Nature Cancer, 2024.
  15. Clonal Lineage Tracing with Somatic Delivery of Recordable Barcodes Reveals Migration Histories of Metastatic Prostate Cancer
    Serio R.N., Scheben A., Lu B., Gargiulo D.V., Patruno L., et al.
    Cancer Discovery, 2024.
  16. Control-FREEC viewer: a tool for the visualization and exploration of copy number variation data
    Crippa V., Fina E., Ramazzotti D., Piazza R.
    BMC Bioinformatics, 2024.
  17. 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
    Ilari A., Cogliati V., Sherif N., Grassilli E., Ramazzotti D., et al.
    Biomedicines, 2024.
  18. Idiopathic erythrocytosis: a germline disease?
    Elli E.M., Mauri M., D’Aliberti D., Crespiatico I., Fontana D., et al.
    Clinical and Experimental Medicine, 2024.
  19. First-hit SETBP1 mutations cause a myeloproliferative disorder with bone marrow fibrosis
    Crespiatico I., Zaghi M., Mastini C., D’Aliberti D., Mauri M., et al.
    Blood, 2024.
  20. Evaluating the performance of large language models in haematopoietic stem cell transplantation decision-making †
    Civettini I., Zappaterra A., Granelli B.M., Rindone G., Aroldi A., et al.
    British Journal of Haematology, 2023.
  21. Contribution of pks+ E. coli mutations to colorectal carcinogenesis †
    Chen B., Ramazzotti D., Heide T., Spiteri I., Fernandez-Mateos J., et al.
    Nature Communications, 2023.
  22. Evolutionary signatures of human cancers revealed via genomic analysis of over 35,000 patients
    Fontana D., Crespiatico I., Crippa V., Malighetti F., Villa M., et al.
    Nature Communications, 2023.
  23. Effects of blocking CD24 and CD47 ‘don’t eat me’ signals in combination with rituximab in mantle-cell lymphoma and chronic lymphocytic leukaemia
    Aroldi A., Mauri M., Ramazzotti D., Villa M., Malighetti F., et al.
    Journal of Cellular and Molecular Medicine, 2023.
  24. Characterization of cancer subtypes associated with clinical outcomes by multi-omics integrative clustering
    Crippa V., Malighetti F., Villa M., Graudenzi A., Piazza R., et al.
    Computers in Biology and Medicine, 2023.
  25. LACE 2.0: an interactive R tool for the inference and visualization of longitudinal cancer evolution
    Ascolani G., Angaroni F., Maspero D., Craighero F., Bhavesh N.L.S., et al.
    BMC Bioinformatics, 2023.
  26. DNA Damage Response (DDR) Is Associated With Treatment-free Remission in Chronic Myeloid Leukemia Patients
    Malighetti F., Arosio G., Manfroni C., Mauri M., Villa M., et al.
    HemaSphere, 2023.
  27. Targeting the immune microenvironment in Waldenström Macroglobulinemia via halting the CD40/CD40-ligand axis
    Sacco A., Desantis V., Celay J., Giustini V., Rigali F., et al.
    Blood, 2023.
  28. Characterization of SARS-CoV-2 Mutational Signatures from 1.5+ Million Raw Sequencing Samples
    Aroldi A., Angaroni F., D’Aliberti D., Spinelli S., Crespiatico I., et al.
    Viruses, 2022.
  29. Pan-cancer landscape of AID-related mutations, composite mutations, and their potential role in the ICI response
    Hernandez-Verdin I., Akdemir K.C., Ramazzotti D., Caravagna G., Labreche K., et al.
    npj Precision Oncology, 2022.
  30. Phenotypic plasticity and genetic control in colorectal cancer evolution
    Househam J., Heide T., Cresswell G.D., Spiteri I., Kimberley C., et al.
    Nature, 2022.
  31. The co-evolution of the genome and epigenome in colorectal cancer
    Heide T., Househam J., Cresswell G.D., Spiteri I., Lynn C., et al.
    Nature, 2022.
  32. SparseSignatures: An R package using LASSO-regularized non-negative matrix factorization to identify mutational signatures from human tumor samples
    Mella L., Lal A., Angaroni F., Maspero D., Piazza R., et al.
    STAR Protocols, 2022.
  33. Early detection and improved genomic surveillance of SARS-CoV-2 variants from deep sequencing data
    Ramazzotti D., Maspero D., Angaroni F., Spinelli S., Antoniotti M., et al.
    iScience, 2022.
  34. Variant calling from scRNA-seq data allows the assessment of cellular identity in patient-derived cell lines
    Ramazzotti D., Angaroni F., Maspero D., Ascolani G., Castiglioni I., et al.
    Nature Communications, 2022.
  35. Large-scale analysis of SARS-CoV-2 synonymous mutations reveals the adaptation to the human codon usage during the virus evolution †
    Ramazzotti D., Angaroni F., Maspero D., Mauri M., D’Aliberti D., et al.
    Virus Evolution, 2022.
  36. LACE: Inference of cancer evolution models from longitudinal single-cell sequencing data †
    Ramazzotti D., Angaroni F., Maspero D., Ascolani G., Castiglioni I., et al.
    Journal of Computational Science, 2022.
  37. VirMutSig: Discovery and assignment of viral mutational signatures from sequencing data
    Maspero D., Angaroni F., Porro D., Piazza R., Graudenzi A., et al.
    STAR Protocols, 2021.
  38. PMCE: efficient inference of expressive models of cancer evolution with high prognostic power
    Angaroni F., Chen K., Damiani C., Caravagna G., Graudenzi A., Ramazzotti D.
    Bioinformatics, 2021.
  39. Investigating the performance of multi-objective optimization when learning Bayesian Networks *
    Cazzaniga P., Nobile M.S., Ramazzotti D.
    Neurocomputing, 2021.
  40. De Novo Mutational Signature Discovery in Tumor Genomes using SparseSignatures
    Lal A., Liu K., Tibshirani R., Sidow A., Ramazzotti D.
    PLOS Computational Biology, 2021.
  41. Learning the structure of Bayesian Networks via the bootstrap †
    Caravagna G., Ramazzotti D.
    Neurocomputing, 2021.
  42. Mutational Signatures and Heterogeneous Host Response Revealed Via Large-Scale Characterization of SARS-COV-2 Genomic Diversity †
    Graudenzi A., Maspero D., Angaroni F., Piazza R., Ramazzotti D.
    iScience, 2021.
  43. VERSO: a comprehensive framework for the inference of robust phylogenies and the quantification of intra-host genomic diversity of viral samples
    Ramazzotti D., Angaroni F., Maspero D., Gambacorti-Passerini C., Antoniotti M., et al.
    Patterns, 2021.
  44. Integrated Genomic, Functional, and Prognostic Characterization of Atypical Chronic Myeloid Leukemia
    Fontana D., Ramazzotti D., Aroldi A., Redaelli S., Magistroni V., et al.
    HemaSphere, 2020.
  45. The Influence of Nutrients Diffusion on a Metabolism-driven Model of a Multi-cellular System
    Maspero D., Damiani C., Antoniotti M., Graudenzi A., Di Filippo M., et al.
    Fundamenta Informaticae, 2020.
  46. Machine learning can accurately predict pre-admission baseline hemoglobin and creatinine in intensive care patients
    Dauvin A., Donado C., Bachtiger P., Huang K.C., Sauer C., et al.
    npj Digital Medicine, 2019.
  47. Assessment of network module identification across complex diseases
    Choobdar S., Ahsen M.E., Crawford J., Tomasoni M., Fang T., et al.
    Nature Methods, 2019.
  48. Comprehensive genomic characterization of breast tumors with BRCA1 and BRCA2 mutations †
    Lal A., Ramazzotti D., Weng Z., Liu K., Ford J.M., et al.
    BMC Medical Genomics, 2019.
  49. Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data
    Ramazzotti D., Graudenzi A., De Sano L., Antoniotti M., Caravagna G.
    BMC Bioinformatics, 2019.
  50. Withholding or withdrawing invasive interventions may not accelerate time to death among dying ICU patients
    Ramazzotti D., Clardy P., Celi L.A., Stone D.J., Rudin R.S.
    PLOS ONE, 2019.
  51. Efficient computational strategies to learn the structure of probabilistic graphical models of cumulative phenomena
    Ramazzotti D., Nobile M.S., Antoniotti M., Graudenzi A.
    Journal of Computational Science, 2018.
  52. Improved survival of cancer patients admitted to the ICU between 2002 and 2011 at a U.S. teaching hospital
    Sauer C., Dong J., Celi L.A., Ramazzotti D.
    Cancer Research and Treatment, 2018.
  53. Multi-omic tumor data reveal diversity of molecular mechanisms that correlate with survival †
    Ramazzotti D., Lal A., Wang B., Batzoglou S., Sidow A.
    Nature Communications, 2018.
  54. Learning the structure of Bayesian Networks: A quantitative assessment of the effect of different algorithmic schemes *
    Beretta S., Castelli M., Goncalves I., Henriques R., Ramazzotti D.
    Complexity, 2018.
  55. Detecting repeated cancer evolution from multi-region tumor sequencing data
    Caravagna G., Giarratano Y., Ramazzotti D., Graham T.A., Sanguinetti G., et al.
    Nature Methods, 2018.
  56. Modeling cumulative biological phenomena with Suppes-Bayes causal networks
    Ramazzotti D., Graudenzi A., Caravagna G., Antoniotti M.
    Evolutionary Bioinformatics, 2018.
  57. Causal Data Science for Financial Stress Testing *
    Gao G., Mishra B., Ramazzotti D.
    Journal of Computational Science, 2018.
  58. SIMLR: A Tool for Large-Scale Genomic Analyses by Multi-Kernel Learning †
    Wang B., Ramazzotti D., De Sano L., Zhu J., Pierson E., et al.
    Proteomics, 2017.
  59. OncoScore: a novel, Internet-based tool to assess the oncogenic potential of genes
    Piazza R., Ramazzotti D., Spinelli R., Pirola A., De Sano L., et al.
    Scientific Reports, 2017.
  60. Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning
    Wang B., Zhu J., Pierson E., Ramazzotti D., Batzoglou S.
    Nature Methods, 2017.
  61. Exposing the Probabilistic Causal Structure of Discrimination *
    Bonchi F., Hajian S., Mishra B., Ramazzotti D.
    International Journal of Data Science and Analytics, 2017.
  62. Design of the TRONCO BioConductor Package for TRanslational ONCOlogy *
    Antoniotti M., Caravagna G., De Sano L., Graudenzi A., Mauri G., et al.
    The R Journal, 2016.
  63. Algorithmic Methods to Infer the Evolutionary Trajectories in Cancer Progression
    Caravagna G., Graudenzi A., Ramazzotti D., Sanz-Pamplona R., De Sano L., et al.
    Proceedings of the National Academy of Sciences, 2016.
  64. TRONCO: an R package for the inference of cancer progression models from heterogeneous genomic data †
    De Sano L., Caravagna G., Ramazzotti D., Graudenzi A., Mauri G., et al.
    Bioinformatics, 2016.
  65. CAPRI: Efficient Inference of Cancer Progression Models from Cross-sectional Data
    Ramazzotti D., Caravagna G., Olde-Loohuis L., Graudenzi A., Korsunsky I., et al.
    Bioinformatics, 2015.
  66. Inferring tree causal models of cancer progression with probability raising †
    Olde-Loohuis L., Caravagna G., Graudenzi A., Ramazzotti D., Mauri G., et al.
    PLOS ONE, 2014.

Books & book chapters

  • Data Pre-processing *
    Malley B., Ramazzotti D., Wu J.T.
    Chapter in: Secondary Analysis of Electronic Health Records, MIT Critical Data, Springer, 2016.

Conference proceedings

  1. Exploring the Solution Space of Cancer Evolution Inference Frameworks for Single-Cell Sequencing Data
    Maspero D., Angaroni F., Patruno L., Ramazzotti D., Posada D., Graudenzi A.
    In: Italian Workshop on Artificial Life and Evolutionary Computation (WIVACE 2022), CCIS, 2023.
  2. cyTRON and cyTRON/JS: two Cytoscape-based applications for the inference of cancer evolution models
    Patruno L., Galimberti E., Ramazzotti D., Caravagna G., De Sano L., et al.
    In: Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB), 2019.
  3. Probabilistic Causal Analysis of Social Influence *
    Bonchi F., Gullo F., Mishra B., Ramazzotti D.
    In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM), 2018.
  4. Combining Bayesian Approaches and Evolutionary Techniques for the Inference of Breast Cancer Networks *
    Beretta S., Castelli M., Goncalves I., Merelli I., Ramazzotti D.
    In: International Conference on Evolutionary Computation Theory and Applications, 2016.
  5. Parallel Implementation of Efficient Search Schemes for the Inference of Cancer Progression Models
    Ramazzotti D., Nobile M.S., Cazzaniga P., Mauri G., Antoniotti M.
    In: IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology, 2016.
  6. A Model of Colonic Crypts using SBML Spatial
    Ramazzotti D., Maj C., Antoniotti M.
    In: Italian Workshop on Artificial Life and Evolutionary Computation (WIVACE 2013), EPTCS 130, 74–78.

Theses

External profiles

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