* 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 1808.04773.
  3. Published

  4. Chen*, Cremona*, Qi, Mitra, Chiaromonte, Makova (2020) Human L1 transposition dynamics unrevealed with functional data analysis. Molecular Biology and Evolution msaa194. Press release
  5. 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
  6. Di Iorio, Chiaromonte, Cremona (2020) On the bias of H-scores for comparing biclusters, and how to correct it. Bioinformatics 36(1): 2955–2957.
  7. 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.
  8. Cremona, Xu, Makova, Reimherr, Chiaromonte, Madrigal (2019) Functional data analysis for computational biology. Bioinformatics 35(17): 2311–2313.
  9. 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
  10. 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.
  11. 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.
  12. 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.
  13. Cremona, Sangalli, Vantini, Dellino, Pelicci, Secchi, Riva (2015) Peak shape clustering reveals biological insights. BMC Bioinformatics 16:349.
  14. Conference proceedings, abstracts and book chapters

  15. 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.
  16. 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.
  17. 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.
  18. 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.
  19. 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.
  20. 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.