Publications

* indicates co-first authors, † indicates co-corresponding authors

    Preprint

  1. Dang, Cremona, Lazar, Chiaromonte (2023) An efficient approach to characterize spatio-temporal dependence in cortical surface fMRI data. arXiv 2312.16346.
  2. Cremona, Doroshenko, Severino (2023) Functional motif discovery in stock market prices. SSRN 4642040.
  3. Cremona, Sarault, Severino (2024) Equity market-neutral strategies using variable selection and regularized regression. Chair Fintech AMF – Finance Montréal research paper.
  4. Torres-Gonzalez, Cremona, Storer, Ventura, O’Neill, Makova (2025) Nuclear mitochondrial sequences in great ape Telomere-to-Telomere genomes. bioRxiv 2025.04. 24.650511.
  5. Fathi, Cremona, Severino (2025) Selection of functional predictors and smooth coefficient estimation for scalar-on-function regression models. arXiv 2506.17773.

    6. Published

  6. Gansou, Oualkacha, Cremona, Lakhal-Chaieb (Accepted) A functional approach to testing overall effect of interaction between DNA methylation and SNPs. Statistics in Medicine.
  7. Boschi*, Di Iorio*, Testa*, Cremona†, Chiaromonte† (2026) Contrasting pre-vaccine COVID-19 waves in Italy through Functional Data Analysis. Scientific Reports 16: 222.
  8. Dang, Cremona, Chiaromonte (2025) smoothEM: a new approach for the simultaneous assessment of smooth patterns and spikes. Electronic Journal of Statistics 19(2): 3835-3866.
  9. Ashouri, Phoa, Cremona (2025) Analyzing Taiwanese traffic patterns on consecutive holidays through forecast reconciliation and prediction-based anomaly detection techniques. IEEE Access 13: 108500-108518.
  10. Neumann*, Zghal*, Cremona, Hajji, Morin, Rekik (2025) A data-driven personalized approach to predict blood glucose levels in type-1 diabetes patients exercising in free-living conditions. Computers in Biology and Medicine 190: 110015.
  11. Di Iorio, Cremona, Chiaromonte (2025) funBIalign: a hierarchical algorithm for functional motif discovery based on mean squared residue scores. Statistics and Computing 35:11.
  12. Catania, Zanini, Cremona, Landa, Musio, Watson, Aleo, Aiken, Sasso, Bagnasco (2024) Nurses’ intention to leave, nurse workload and in-hospital patient mortality in Italy: a descriptive and regression study. Health Policy 143: 105032.
  13. Cremona, Chiaromonte (2023) Probabilistic K-means with local alignment for clustering and motif discovery in functional data. Journal of Computational and Graphical Statistics 32(3): 1119-1130. Accepted manuscript
  14. Weissensteiner*, Cremona*, Guiblet, Stoler, Harris, Cechova, Eckert, Chiaromonte, Huang, Makova (2023) Accurate sequencing of DNA motifs able to form alternative (non-B) structures. Genome Research 33: 907-922.
  15. Jalili†, Cremona†, Palluzzi† (2023) Rescuing biologically relevant consensus regions across replicated samples. BMC Bioinformatics 24: 240.
  16. Severino, Cremona, Dadié (2022) COVID-19 effects on the Canadian term structure of interest rates. Review of Economic Analysis 14(4): 473-502.
  17. Arbeithuber, Cremona, Hester, Barrett, Higgins, Anthony, Chiaromonte, Diaz, Makova (2022) Advanced age increases frequencies of de novo mitochondrial mutations in macaque oocytes and somatic tissues. Proceedings of the National Academy of Sciences 119(15): e2118740119. Press release
  18. Boschi, Di Iorio, Testa, Cremona†, Chiaromonte† (2021) Functional data analysis characterizes the shapes of the first COVID-19 epidemic wave in Italy. Scientific Reports 11: 17054. Audio summary, Press release 1, Press release 2
  19. Guiblet*, Cremona*, Harris, Chen, Eckert, Chiaromonte†, Huang†, Makova† (2021) Non-B DNA: a major contributor to small- and large-scale variation in nucleotide substitution frequencies across the genome. Nucleic Acids Research 49(3): 1497–1516. Press release
  20. Chen*, Cremona*, Qi, Mitra, Chiaromonte†, Makova† (2020) Human L1 transposition dynamics unrevealed with functional data analysis. Molecular Biology and Evolution 37(12): 3576–3600. Press release
  21. Arbeithuber, Hester, Cremona, Stoler, Zaidi, Higgins, Anthony, Chiaromonte, Diaz, Makova (2020) Age-related accumulation of de novo mitochondrial mutations in mammalian oocytes and somatic tissues. PLoS Biology 18(7): e3000745. Press release
  22. Di Iorio, Chiaromonte, Cremona (2020) On the bias of H-scores for comparing biclusters, and how to correct it. Bioinformatics 36(9): 2955–2957.
  23. Mei, Arbeithuber, Cremona, DeGiorgio, Nekrutenko (2019) A high resolution view of adaptive event dynamics in a plasmid. Genome Biology and Evolution 11(10): 3022–3034.
  24. Cremona, Xu, Makova, Reimherr, Chiaromonte, Madrigal (2019) Functional data analysis for computational biology. Bioinformatics 35(17): 2311–2313.
  25. Guiblet*, Cremona*, Cechova, Harris, Kejnovska, Kejnovsky, Eckert, Chiaromonte†, Makova† (2018) Long-read sequencing technology indicates genome-wide effects of non-B DNA on polymerization speed and error rate. Genome Research, 28: 1767-1778. Press release
  26. Cremona*, Pini*, Cumbo, Makova, Chiaromonte†, Vantini† (2018) IWTomics: testing high-resolution sequence-based “Omics” data at multiple locations and scales. Bioinformatics 34(13): 2289–2291.
  27. Campos-Sànchez*, Cremona*, Pini, Chiaromonte†, Makova† (2016) Integration and fixation preferences of human and mouse endogenous retroviruses uncovered with functional data analysis. PLoS Computational Biology 12(6): e1004956.
  28. Cremona, Liu, Hu, Bruni, Lewis (2016) Predicting railway wheel wear under uncertainty of wear coefficient, using universal kriging. Reliability Engineering and System Safety 154: 49-59. Accepted manuscript
  29. Cremona, Sangalli, Vantini, Dellino, Pelicci, Secchi, Riva (2015) Peak shape clustering reveals biological insights. BMC Bioinformatics 16:349.

    30. Conference proceedings, abstracts and book chapters

  30. Nuemann, Cremona, Hajji, Morin, Rekik (2026) Exploring the recent applications of artificial intelligence techniques for type-1 diabetes management. In book: Operations Research and Artificial Intelligence in Healthcare Management (editors: Landa, Côté, Gartner, Layani, Husson, Capgras, Lemaire). Springer.
  31. Di Iorio, Cremona, Chiaromonte (2025) Amplitude-invariant functional motif discovery. In book: New Trends in Functional Statistics and Related Fields (editors: Aneiros, Bongiorno, Goia, Hušková). Springer.
  32. Arbeithuber, Hester, Cremona, Barret, Higgins, Anthony, Diaz, Makova (2021) Advanced age increases frequencies of de novo mitochondrial mutations in macaque oocytes and somatic tissues. Abstracts from the Environmental Mutagenesis and Genomics Society 52nd Annual Meeting. Environmental and Molecular Mutagenesis 62(S1): 52-52.
  33. Torres-Gonzalez, Arbeithuber, Hester, Cremona, Stoler, Higgins, Anthony, Chiaromonte, Diaz, Makova (2020) Duplex sequencing uncovers age-related increase in the frequency of de novo indels in mouse mitochondrial DNA. Abstracts from the 53rd European Society of Human Genetics (ESHG) Conference: e-Posters. European Journal of Human Genetics 28(S1): 1007-1008.
  34. Eckert, Hile, Guiblet, Cremona, Stein, Huang, Chiaromonte, Makova (2020) G-quadruplex sequences are barriers to replicative DNA polymerases and hotspots of mutagenesis. Abstracts from the Environmental Mutagenesis and Genomics Society 51st Annual Meeting. Environmental and Molecular Mutagenesis 61(S1): 47-47.
  35. Cremona, Campos-Sànchez, Pini, Vantini, Makova, Chiaromonte (2017) Functional data analysis of “Omics” data: how does the genomic landscape influence integration and fixation of endogenous retroviruses? In book: Functional Statistics and Related Fields (editors: Aneiros, Bongiorno, Cao, Vieu). Springer.
  36. Cremona, Campos-Sànchez, Pini, Vantini, Makova, Chiaromonte (2016) Functional data analysis at the boundary of “Omics”. Proceedings of IWSM 2016, 31st International Workshop on Statistical Modelling.
  37. Azzimonti, Cremona, Ghiglietti, Ieva, Menafoglio, Pini, Zanini (2015) BARCAMP: Technology foresight and statistics for the future. In book: Advances in Complex Data Modeling and Computational Methods in Statistics (editors: Paganoni, Secchi). Springer.
  38. Cremona, Pelicci, Riva, Sangalli, Secchi, Vantini (2014) Cluster analysis on shape indices for ChIP-seq data. Proceedings of SIS 2014, 47th Scientific Meeting of the Italian Statistical Society.
  39. Cremona, Riva, Sangalli, Secchi, Vantini (2013) Clustering ChIP-seq data using peak shape. Proceedings of SCo 2013, 8th Conference on Complex Data Modeling and Computationally Intensive Statistical Methods for Estimation and Prediction.