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


  1. Cremona, Chiaromonte (2020) Probabilistic K-mean with local alignment for clustering and motif discovery in functional data. arXiv 1808.04773.
  2. Boschi, Di Iorio, Testa, Cremona, Chiaromonte (2020) The shapes of an epidemic: using functional data analysis to characterize COVID-19 in Italy. arXiv 2800.04700.
  3. Severino, Cremona, Dadié (2021) COVID-19 effects on the Canadian term structure of interest rates. SSRN 3762628.
  4. Published

  5. 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
  6. 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
  7. 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
  8. Di Iorio, Chiaromonte, Cremona (2020) On the bias of H-scores for comparing biclusters, and how to correct it. Bioinformatics 36(9): 2955–2957.
  9. 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.
  10. Cremona, Xu, Makova, Reimherr, Chiaromonte, Madrigal (2019) Functional data analysis for computational biology. Bioinformatics 35(17): 2311–2313.
  11. 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
  12. 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.
  13. 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.
  14. 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.
  15. Cremona, Sangalli, Vantini, Dellino, Pelicci, Secchi, Riva (2015) Peak shape clustering reveals biological insights. BMC Bioinformatics 16:349.
  16. Conference proceedings, abstracts and book chapters

  17. 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.
  18. 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.
  19. 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.
  20. 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.
  21. 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.
  22. 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.
  23. 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.