Methodological and computational statistics
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Signorelli, M. (accepted). pencal: an R Package
for the Dynamic Prediction of Survival with Many Longitudinal
Predictors. To appear in: The R Journal.
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Signorelli, M., Retif, S. (in review). Benchmarking
multi-step methods for the dynamic prediction of survival with numerous
longitudinal predictors. Preprint: arXiv:2403.14336.
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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.
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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.
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Gomon, D., Simons, J. et al. [including Signorelli, M.]
(2023). Inspecting
the quality of care: a comparison of CUSUM methods for inter hospital
performance. Health Services and Outcomes Research
Methodology.
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Signorelli, M., Cutillo, L. (2022). On community structure
validation in real networks. Computational Statistics, 37,
1165–1183.
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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.
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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.
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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).
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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.
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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.
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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.
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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
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Signorelli, M., Tsonaka, R., Aartsma-Rus, A., Spitali,
P. (2023). Multiomic
characterization of disease progression in mice lacking dystrophin.
PLOS ONE, 18(3), e0283869.
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Johansson, C., Junt, H., Signorelli, M. et al. (2023).
Orthogonal
proteomics methods warrant the development of Duchenne Muscular
Dystrophy biomarkers. Clinical Proteomics, 20, 23.
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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.
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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.
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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.
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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.
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Signorelli, M., Mason, A. et al. (2020). Evaluation of
blood gene expression levels in facioscapulohumeral muscular dystrophy
patients. Scientific Reports, 10, 17547.
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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.
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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.
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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.
Conference proceedings and technical reports
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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.
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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.
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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.