Methodological and computational statistics

  1. Li, X., Signorelli, M. (in review). A Comparison of R Packages for Estimating Generalized Linear Mixed Models.
    Preprint: arXiv:2606.15933.
  2. Cipriani, M., Alfò, M., Signorelli, M. (in review). Dynamic Prediction in Mixture Cure Models: A Model-Based Landmarking Approach. Preprint: arXiv:2509.18875.
  3. Signorelli, M., Retif, S. (2025). Benchmarking multi-step methods for the dynamic prediction of survival with numerous longitudinal predictors. The International Journal of Biostatistics.
  4. Signorelli, M. (2024). pencal: an R Package for the Dynamic Prediction of Survival with Many Longitudinal Predictors. The R Journal, 16 (2), 134-153.
  5. Gomon, D., Putter, H., Fiocco, M., Signorelli, M. (2024). Dynamic prediction of survival using multivariate Functional Principal Component Analysis: a strict landmarking approach. Statistical Methods in Medical Research, 33 (2), 256-272.
  6. Gomon, D., Simons, J. et al. [including Signorelli, M.] (2024). Inspecting the quality of care: a comparison of CUSUM methods for inter hospital performance. Health Services and Outcomes Research Methodology, 24, 281-303.
  7. Gomon, D., Fiocco, M., Putter, H., Signorelli, M. (2023). SUrvival Control Chart EStimation Software in R: the success package. The R Journal, 15 (4), 270-291.
  8. Signorelli, M., Cutillo, L. (2022). On community structure validation in real networks. Computational Statistics, 37, 1165–1183.
  9. Signorelli, M., Spitali, P., Al-Khalili Szigyarto, C., The Mark-MD Consortium, Tsonaka, R. (2021). Penalized regression calibration: A method for the prediction of survival outcomes using complex longitudinal and high-dimensional data. Statistics in Medicine, 40 (27), 6178-6196.
    A short version of this article was awarded the Young Statistician Award by the International Biometric Society.
  10. Signorelli, M., Spitali, P., Tsonaka, R. (2021). Poisson-Tweedie mixed-effects model: a flexible approach for the analysis of longitudinal RNA-seq data. Statistical Modelling, 21 (6), 520-545.
    This article received a honourable mention from the jury of the Hans van Houwelingen Award.
  11. Signorelli, M., Wit, E. C. (2020). Model-based clustering for populations of networks. Statistical Modelling, 20 (1), 9-29.
    A conference proceedings version of this article was awarded the Best Student Paper Award of the Statistical Modelling Society (SMS) at the 32nd International Workshop on Statistical Modelling (IWSM, 2017).
  12. Signorelli, M., Wit, E. C. (2018). A penalized inference approach to stochastic block modelling of community structure in the Italian Parliament. Journal of the Royal Statistical Society: Series C (Applied Statistics), 67 (2), 355-369.
  13. Signorelli, M. (2017). Inferring Community-driven Structure in Complex Networks. University of Groningen and University of Padova. ISBN: 978-90-367-9575-3.
    NB: This is my doctoral dissertation. Promotors: Ernst Wit and Monica Chiogna.
    My PhD thesis was awarded the Best PhD Thesis Award of the Italian Statistical Society (SIS) in the category Applied Statistics.
  14. Signorelli, M. (2017). Variable selection for (realistic) stochastic blockmodels. In: Petrucci, A., Verde, R. (editors), SIS 2017. Statistics and Data Science: new challenges, new generations, pp. 927-934. Firenze University Press. ISBN: 978-88-6453-521-0.
  15. Signorelli, M., Vinciotti, V., Wit, E. C. (2016). NEAT: an efficient network enrichment analysis test. BMC Bioinformatics, 17, 352.
    With this article I won the competition for the Research Award 2016 of the Department of Statistical Sciences of the University of Padova.

Applied statistics

  1. Signorelli, M., Tsonaka, R., Aartsma-Rus, A., Spitali, P. (2023). Multiomic characterization of disease progression in mice lacking dystrophin. PLOS ONE, 18(3), e0283869.
  2. Johansson, C., Junt, H., Signorelli, M. et al. (2023). Orthogonal proteomics methods warrant the development of Duchenne Muscular Dystrophy biomarkers. Clinical Proteomics, 20, 23.
  3. van de Velde, M., Koeks, Z., Signorelli, M. et al. (2023). Longitudinal Assessment of Creatine Kinase, Creatine/Creatinine ratio and Myostatin as Monitoring Biomarkers in Becker Muscular Dystrophy. Neurology, 100 (9), e975-e984.
  4. Signorelli, M., Ebrahimpoor, M. et al. (2021). Peripheral blood transcriptome profiling enables monitoring disease progression in dystrophic mice and patients. EMBO Molecular Medicine, 13, e13328.
  5. Koeks, Z., Janson, A. A. et al. [including Signorelli, M.] (2021). Low dystrophin variability between muscles and stable expression over time in Becker muscular dystrophy using capillary western immunoassay. Scientific Reports, 11, 5952.
  6. Signorelli, M., Ayoglu, B. et al. (2020). Longitudinal serum biomarker screening identifies malate dehydrogenase 2 as candidate prognostic biomarker for Duchenne muscular dystrophy. Journal of Cachexia, Sarcopenia and Muscle, 11, 505-517.
  7. Signorelli, M., Mason, A. et al. (2020). Evaluation of blood gene expression levels in facioscapulohumeral muscular dystrophy patients. Scientific Reports, 10, 17547.
  8. Tsonaka, R., Signorelli, M., Sabir, E., Seyer, A., Hettne, K., Aartsma-Rus, A., Spitali, P. (2020). Longitudinal metabolomic analysis of plasma enables modelling disease progression in Duchenne muscular dystrophy mouse models. Human Molecular Genetics, 29 (5), 745-755.
  9. Previtali, S., Gidaro, T. et al [including Signorelli, M.] (2020). Rimeporide as a first- in-class NHE-1 inhibitor: Results of a Phase Ib trial in young patients with Duchenne Muscular Dystrophy. Pharmacological Research, 159, 104999.
  10. Strandberg, K., Ayoglu, B. et al [including Signorelli, M.] (2020). Blood-derived biomarkers correlate with clinical progression in Duchenne muscular dystrophy. Journal of Neuromuscular Diseases, 7 (3), 231-246.

Outreach

  1. Fortuna, M., Vitalini, F., Signorelli, M. et al. (2020). e-Rum2020: how we turned a physical conference into a successful virtual event. The R Journal, 12 (2), 416-424.

Conference proceedings and technical reports

  1. Carvisiglia, L., Ranciati, L., Signorelli, M., (2025). On the Computation of Prediction Intervals for Survival Times in Dynamic Prediction Problems.
    In: Martella, F., Arima, S., Marino, M.F., Mollica, C. (eds). Statistical Science: From Theory to Applied Research II, Springer.

    Statistical Science: From Theory to Applied Research II, vol. 1, pp. 471-474.
  2. Cipriani, M., Alfò, M., Signorelli, M., (2025). Extending landmarking to mixture cure models with time-varying covariates. Proceedings of the 39th International Workshop on Statistical Modelling, vol. 1, pp. 471-474.
  3. Signorelli, M., Wit, E. C. (2017). Model-based clustering for populations of networks. Proceedings of the 32nd International Workshop on Statistical Modelling, vol. 1, pp. 155-160.
  4. Signorelli, M., Vinciotti, V., Wit, E. C. (2016). NEAT: an efficient Network Enrichment Analysis Test. Proceedings of the 31st International Workshop on Statistical Modelling, vol. 1, pp. 289-294.
  5. Minotti S. C., Signorelli, M. (2013). Il modello Tucker3 per l’analisi dei bilanci dei comuni italiani. Technical report number 241 of the Department of Statistics and Quantitative Methods of the University of Milan-Bicocca.