Alex Graudenzi
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Explore the profile of Alex Graudenzi including associated specialties, affiliations and a list of published articles.
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38
Citations
447
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Recent Articles
1.
Punzi S, Cittaro D, Gatti G, Crupi G, Botrugno O, Cartalemi A, et al.
Nat Commun
. 2025 Feb;
16(1):1291.
PMID: 39900637
Bacteria withstand antibiotic treatment through three alternative mechanisms: resistance, persistence or tolerance. While resistance and persistence have been described, whether drug-induced tolerance exists in cancer cells remains largely unknown. Here,...
2.
Patruno L, Milite S, Bergamin R, Calonaci N, dOnofrio A, Anselmi F, et al.
PLoS Comput Biol
. 2023 Nov;
19(11):e1011557.
PMID: 37917660
Single-cell RNA and ATAC sequencing technologies enable the examination of gene expression and chromatin accessibility in individual cells, providing insights into cellular phenotypes. In cancer research, it is important to...
3.
Fontana D, Crespiatico I, Crippa V, Malighetti F, Villa M, Angaroni F, et al.
Nat Commun
. 2023 Sep;
14(1):5982.
PMID: 37749078
Recurring sequences of genomic alterations occurring across patients can highlight repeated evolutionary processes with significant implications for predicting cancer progression. Leveraging the ever-increasing availability of cancer omics data, here we...
4.
Crippa V, Malighetti F, Villa M, Graudenzi A, Piazza R, Mologni L, et al.
Comput Biol Med
. 2023 Jun;
162:107064.
PMID: 37267828
Cancer patients show heterogeneous phenotypes and very different outcomes and responses even to common treatments, such as standard chemotherapy. This state-of-affairs has motivated the need for the comprehensive characterization of...
5.
LACE 2.0: an interactive R tool for the inference and visualization of longitudinal cancer evolution
Ascolani G, Angaroni F, Maspero D, Craighero F, Bhavesh N, Piazza R, et al.
BMC Bioinformatics
. 2023 Mar;
24(1):99.
PMID: 36932333
Background: Longitudinal single-cell sequencing experiments of patient-derived models are increasingly employed to investigate cancer evolution. In this context, robust computational methods are needed to properly exploit the mutational profiles of...
6.
Aroldi A, Angaroni F, DAliberti D, Spinelli S, Crespiatico I, Crippa V, et al.
Viruses
. 2023 Jan;
15(1).
PMID: 36680048
We present a large-scale analysis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) substitutions, considering 1,585,456 high-quality raw sequencing samples, aimed at investigating the existence and quantifying the effect of...
7.
Angaroni F, Guidi A, Ascolani G, dOnofrio A, Antoniotti M, Graudenzi A
BMC Bioinformatics
. 2022 Jul;
23(1):269.
PMID: 35804300
Background: The combined effects of biological variability and measurement-related errors on cancer sequencing data remain largely unexplored. However, the spatio-temporal simulation of multi-cellular systems provides a powerful instrument to address...
8.
Mella L, Lal A, Angaroni F, Maspero D, Piazza R, Sidow A, et al.
STAR Protoc
. 2022 Jul;
3(3):101513.
PMID: 35779264
We outline the features of the R package SparseSignatures and its application to determine the signatures contributing to mutation profiles of tumor samples. We describe installation details and illustrate a...
9.
Ramazzotti D, Maspero D, Angaroni F, Spinelli S, Antoniotti M, Piazza R, et al.
iScience
. 2022 Jun;
25(6):104487.
PMID: 35677393
A key task of genomic surveillance of infectious viral diseases lies in the early detection of dangerous variants. Unexpected help to this end is provided by the analysis of deep...
10.
Ramazzotti D, Angaroni F, Maspero D, Ascolani G, Castiglioni I, Piazza R, et al.
Nat Commun
. 2022 May;
13(1):2718.
PMID: 35551450
No abstract available.