Publications

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

    Preprint

  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.
  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 and book chapters

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