Fabrizio Angaroni

475 total citations
22 papers, 200 citations indexed

About

Fabrizio Angaroni is a scholar working on Molecular Biology, Cancer Research and Genetics. According to data from OpenAlex, Fabrizio Angaroni has authored 22 papers receiving a total of 200 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Molecular Biology, 7 papers in Cancer Research and 6 papers in Genetics. Recurrent topics in Fabrizio Angaroni's work include Cancer Genomics and Diagnostics (7 papers), Evolution and Genetic Dynamics (6 papers) and Single-cell and spatial transcriptomics (4 papers). Fabrizio Angaroni is often cited by papers focused on Cancer Genomics and Diagnostics (7 papers), Evolution and Genetic Dynamics (6 papers) and Single-cell and spatial transcriptomics (4 papers). Fabrizio Angaroni collaborates with scholars based in Italy, United States and Germany. Fabrizio Angaroni's co-authors include Alex Graudenzi, Davide Maspero, Daniele Ramazzotti, Rocco Piazza, Marco Antoniotti, Giuliano Benenti, G. Strini, Simone Montangero, Tommaso Calarco and Carlo Gambacorti‐Passerini and has published in prestigious journals such as Nature Communications, BMC Bioinformatics and Neurocomputing.

In The Last Decade

Fabrizio Angaroni

19 papers receiving 199 citations

Peers

Fabrizio Angaroni

GENET

Davide Maspero Italy

Nicolae Sapoval United States

Hesam Montazeri Iran

Camilla Pang United Kingdom

Hassaan Maan Canada

Ales Varabyou United States

Kaibo Liu United States

Yury D. Nechipurenko Russia

Anat Zimmer Israel

Juan Rodriguez-Rivas France

Davide Maspero Italy

Fabrizio Angaroni

222 ×2.0

78 ×1.0

71 ×1.9

25 ×0.9

GENET

25 ×1.0

Citations per year, relative to Fabrizio Angaroni Fabrizio Angaroni (= 1×) peers Davide Maspero

Countries citing papers authored by Fabrizio Angaroni

Since Specialization

Citations

This map shows the geographic impact of Fabrizio Angaroni's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Fabrizio Angaroni with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fabrizio Angaroni more than expected).

Fields of papers citing papers by Fabrizio Angaroni

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Fabrizio Angaroni. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Fabrizio Angaroni. The network helps show where Fabrizio Angaroni may publish in the future.

Co-authorship network of co-authors of Fabrizio Angaroni

This figure shows the co-authorship network connecting the top 25 collaborators of Fabrizio Angaroni. A scholar is included among the top collaborators of Fabrizio Angaroni based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Fabrizio Angaroni. Fabrizio Angaroni is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Angaroni, Fabrizio, et al.. (2025). Translating microbial kinetics into quantitative responses and testable hypotheses using Kinbiont. Nature Communications. 16(1). 6440–6440.

Fontana, Diletta, Fabrizio Angaroni, De Luca, et al.. (2023). Evolutionary signatures of human cancers revealed via genomic analysis of over 35,000 patients. Nature Communications. 14(1). 5982–5982. 2 indexed citations

Angaroni, Fabrizio, Davide Maspero, Rocco Piazza, et al.. (2023). LACE 2.0: an interactive R tool for the inference and visualization of longitudinal cancer evolution. BMC Bioinformatics. 24(1). 99–99.

Ramazzotti, Daniele, Fabrizio Angaroni, Davide Maspero, et al.. (2022). Large-scale analysis of SARS-CoV-2 synonymous mutations reveals the adaptation to the human codon usage during the virus evolution. Virus Evolution. 8(1). veac026–veac026. 17 indexed citations

Angaroni, Fabrizio, Kevin Chen, C. Damiani, et al.. (2022). PMCE: efficient inference of expressive models of cancer evolution with high prognostic power. BOA (University of Milano-Bicocca). 8 indexed citations

Angaroni, Fabrizio, et al.. (2022). J-SPACE: a Julia package for the simulation of spatial models of cancer evolution and of sequencing experiments. BMC Bioinformatics. 23(1). 269–269. 3 indexed citations

Ramazzotti, Daniele, Davide Maspero, Fabrizio Angaroni, et al.. (2022). Early detection and improved genomic surveillance of SARS-CoV-2 variants from deep sequencing data. iScience. 25(6). 104487–104487. 2 indexed citations

Lal, Avantika, Fabrizio Angaroni, Davide Maspero, et al.. (2022). SparseSignatures: An R package using LASSO-regularized non-negative matrix factorization to identify mutational signatures from human tumor samples. STAR Protocols. 3(3). 101513–101513. 1 indexed citations

Aroldi, Andrea, et al.. (2022). Characterization of SARS-CoV-2 Mutational Signatures from 1.5+ Million Raw Sequencing Samples. Viruses. 15(1). 7–7. 2 indexed citations

10.

Ramazzotti, Daniele, Fabrizio Angaroni, Davide Maspero, et al.. (2021). VERSO: A comprehensive framework for the inference of robust phylogenies and the quantification of intra-host genomic diversity of viral samples. Patterns. 2(3). 100212–100212. 21 indexed citations

11.

Graudenzi, Alex, Davide Maspero, Fabrizio Angaroni, Rocco Piazza, & Daniele Ramazzotti. (2021). Mutational signatures and heterogeneous host response revealed via large-scale characterization of SARS-CoV-2 genomic diversity. iScience. 24(2). 102116–102116. 51 indexed citations

12.

Maspero, Davide, Fabrizio Angaroni, Danilo Porro, et al.. (2021). VirMutSig: Discovery and assignment of viral mutational signatures from sequencing data. STAR Protocols. 2(4). 100911–100911. 2 indexed citations

13.

Ramazzotti, Daniele, Fabrizio Angaroni, Davide Maspero, et al.. (2021). LACE: Inference of cancer evolution models from longitudinal single-cell sequencing data. Journal of Computational Science. 58. 101523–101523. 12 indexed citations

14.

Maspero, Davide, Fabrizio Angaroni, C. Damiani, et al.. (2021). On the Use of Topological Features of Metabolic Networks for the Classification of Cancer Samples. Current Genomics. 22(2). 88–97. 1 indexed citations

15.

Angaroni, Fabrizio, Alex Graudenzi, Marco Rossignolo, et al.. (2020). An Optimal Control Framework for the Automated Design of Personalized Cancer Treatments. Frontiers in Bioengineering and Biotechnology. 8. 523–523. 16 indexed citations

16.

Maspero, Davide, et al.. (2020). A review of computational strategies for denoising and imputation of single-cell transcriptomic data. Briefings in Bioinformatics. 22(4). 35 indexed citations

17.

Angaroni, Fabrizio, Francesca D’Avila, Andrea Conti, et al.. (2018). gDNA qPCR is statistically more reliable than mRNA analysis in detecting leukemic cells to monitor CML. Cell Death and Disease. 9(3). 349–349. 6 indexed citations

18.

Angaroni, Fabrizio, Giuliano Benenti, & G. Strini. (2018). Applications of Picard and Magnus expansions to the Rabi model. The European Physical Journal D. 72(10). 6 indexed citations

19.

Angaroni, Fabrizio, et al.. (2017). Amplification of the parametric dynamical Casimir effect via optimal control. Physical review. A. 96(3). 8 indexed citations

20.

Angaroni, Fabrizio, Giuliano Benenti, & G. Strini. (2016). Reconstruction of electromagnetic field states by a probe qubit. The European Physical Journal D. 70(10). 2 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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