1. Cremona, Chiaromonte (2018) Probabilistic K-mean with local alignment for clustering and motif discovery in functional data. arXiv 1808.04773.
  2. Mei, Arbeithuber, Cremona, DeGiorgio, Nekrutenko (2018) A high resolution view of adaptive events. bioRxiv 429175
  3. Di Iorio, Chiaromonte, Cremona (2019) On the bias of H-scores for comparing biclusters, and how to correct it. arXiv 1907.11142.
  4. Published

  5. Cremona, Xu, Makova, Reimherr, Chiaromonte, Madrigal (2019) Functional data analysis for computational biology. Bioinformatics 35(17): 2311–2313.
  6. 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
  7. 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.
  8. 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.
  9. 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.
  10. Cremona, Sangalli, Vantini, Dellino, Pelicci, Secchi, Riva (2015) Peak shape clustering reveals biological insights. BMC Bioinformatics 16:349.
  11. Conference proceedings and book chapters

  12. 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.
  13. 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.
  14. 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.
  15. 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.
  16. 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.

* indicates co-first authors.