Marco Beccuti

4.2k total citations
98 papers, 1.5k citations indexed

About

Marco Beccuti is a scholar working on Molecular Biology, Control and Systems Engineering and Computational Theory and Mathematics. According to data from OpenAlex, Marco Beccuti has authored 98 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 45 papers in Molecular Biology, 19 papers in Control and Systems Engineering and 18 papers in Computational Theory and Mathematics. Recurrent topics in Marco Beccuti's work include Gene Regulatory Network Analysis (18 papers), Petri Nets in System Modeling (15 papers) and Advanced DC-DC Converters (11 papers). Marco Beccuti is often cited by papers focused on Gene Regulatory Network Analysis (18 papers), Petri Nets in System Modeling (15 papers) and Advanced DC-DC Converters (11 papers). Marco Beccuti collaborates with scholars based in Italy, Switzerland and France. Marco Beccuti's co-authors include Manfred Morari, Francesca Cordero, Raffaele Calogero, Georgios Papafotiou, Sébastien Mariéthoz, Susanna Donatelli, Shu Wang, G. Franceschinis, Maddalena Arigoni and Matteo Carrara and has published in prestigious journals such as Nature Communications, Bioinformatics and PLoS ONE.

In The Last Decade

Marco Beccuti

96 papers receiving 1.5k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Marco Beccuti Italy 20 535 430 405 262 215 98 1.5k
Hua Lin China 18 645 1.2× 426 1.0× 313 0.8× 113 0.4× 124 0.6× 40 1.5k
Young Hwan Chang United States 19 423 0.8× 209 0.5× 73 0.2× 101 0.4× 492 2.3× 105 1.4k
Zhenyong Zhang China 26 496 0.9× 660 1.5× 430 1.1× 171 0.7× 184 0.9× 128 2.3k
Zeynep H. Gümüş United States 24 1.0k 1.9× 376 0.9× 42 0.1× 535 2.0× 408 1.9× 55 2.4k
Qiao Liu China 23 1.1k 2.0× 79 0.2× 89 0.2× 195 0.7× 112 0.5× 117 1.9k
He Jiang China 29 247 0.5× 887 2.1× 437 1.1× 74 0.3× 72 0.3× 98 2.3k
Minghu Jiang China 16 400 0.7× 98 0.2× 60 0.1× 156 0.6× 192 0.9× 91 1.1k
Saurav Mallik India 27 832 1.6× 41 0.1× 133 0.3× 289 1.1× 95 0.4× 188 2.1k
Yiming Ye United States 23 526 1.0× 84 0.2× 327 0.8× 50 0.2× 57 0.3× 98 1.9k
Martin Mönnigmann Germany 19 368 0.7× 716 1.7× 119 0.3× 22 0.1× 100 0.5× 145 1.5k

Countries citing papers authored by Marco Beccuti

Since Specialization
Citations

This map shows the geographic impact of Marco Beccuti'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 Marco Beccuti with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marco Beccuti more than expected).

Fields of papers citing papers by Marco Beccuti

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Marco Beccuti. 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 Marco Beccuti. The network helps show where Marco Beccuti may publish in the future.

Co-authorship network of co-authors of Marco Beccuti

This figure shows the co-authorship network connecting the top 25 collaborators of Marco Beccuti. A scholar is included among the top collaborators of Marco Beccuti 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 Marco Beccuti. Marco Beccuti is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Arigoni, Maddalena, Federica Riccardo, Elisa Balmas, et al.. (2024). A single cell RNAseq benchmark experiment embedding “controlled” cancer heterogeneity. Scientific Data. 11(1). 159–159. 3 indexed citations
2.
Bergandi, Loredana, Gianluca Gennarelli, Marco Beccuti, et al.. (2024). A novel machine-learning framework based on early embryo morphokinetics identifies a feature signature associated with blastocyst development. Journal of Ovarian Research. 17(1). 63–63. 9 indexed citations
3.
Grassi, Elena, Francesco Sassi, Raffaele Calogero, et al.. (2023). CONNECTOR, fitting and clustering of longitudinal data to reveal a new risk stratification system. Bioinformatics. 39(5). 6 indexed citations
4.
Valdembri, Donatella, Guido Serini, Federica Riccardo, et al.. (2023). OmniReprodubileCellAnalysis: a comprehensive toolbox for the analysis of cellular biology data. 1 indexed citations
5.
6.
Cordero, Francesca, Marco Beccuti, Maddalena Arigoni, et al.. (2021). Sparsely-connected autoencoder (SCA) for single cell RNAseq data mining. npj Systems Biology and Applications. 7(1). 1–1. 28 indexed citations
7.
Arigoni, Maddalena, Silvia Benvenuti, Davide Cacchiarelli, et al.. (2021). MET Exon 14 Skipping: A Case Study for the Detection of Genetic Variants in Cancer Driver Genes by Deep Learning. International Journal of Molecular Sciences. 22(8). 4217–4217. 6 indexed citations
8.
Beccuti, Marco, et al.. (2021). Sparsely Connected Autoencoders: A Multi-Purpose Tool for Single Cell omics Analysis. International Journal of Molecular Sciences. 22(23). 12755–12755. 12 indexed citations
9.
Povero, Massimiliano, Lorenzo Pradelli, Daniela Paolotti, et al.. (2020). A computational framework for modeling and studying pertussis epidemiology and vaccination. BMC Bioinformatics. 21(S8). 344–344. 10 indexed citations
10.
Maglione, Alessandro, Marzio Pennisi, Francesco Pappalardo, et al.. (2020). Computational modeling of the immune response in multiple sclerosis using epimod framework. BMC Bioinformatics. 21(S17). 550–550. 11 indexed citations
11.
Ferrero, Giulio, Valentina Miano, Raffaele Calogero, et al.. (2019). Docker4Circ: A Framework for the Reproducible Characterization of circRNAs from RNA-Seq Data. International Journal of Molecular Sciences. 21(1). 293–293. 8 indexed citations
12.
Cordero, Francesca, Marco Beccuti, Maddalena Arigoni, et al.. (2019). rCASC: reproducible classification analysis of single-cell sequencing data. GigaScience. 8(9). 23 indexed citations
13.
Mozgunov, Pavel, et al.. (2018). A review of the deterministic and diffusion approximations for stochastic chemical reaction networks. Reaction Kinetics Mechanisms and Catalysis. 123(2). 289–312. 12 indexed citations
14.
Amparore, Elvio Gilberto, Marco Beccuti, Marco Botta, Susanna Donatelli, & Fabio Tango. (2018). Adaptive artificial co-pilot as enabler for autonomous vehicles and intelligent transportation systems. International Joint Conference on Artificial Intelligence. 2129. 70–77. 2 indexed citations
15.
Panero, Riccardo, Maddalena Arigoni, Martina Olivero, et al.. (2018). Reproducible bioinformatics project: a community for reproducible bioinformatics analysis pipelines. BMC Bioinformatics. 19(S10). 349–349. 32 indexed citations
16.
Miglio, Gianluca, Alessia Romano, Antonio Di Sabatino, et al.. (2014). The Mode of Action of Dimethyl Fumarate: Protein Succination and Anti-Pyroptotic Effects. Pharmacology. 12(3). 1–2. 4 indexed citations
17.
Fornari, Chiara, Marco Beccuti, Stefania Lanzardo, et al.. (2014). A Mathematical-Biological Joint Effort to Investigate the Tumor-Initiating Ability of Cancer Stem Cells. PLoS ONE. 9(9). e106193–e106193. 15 indexed citations
18.
Cordero, Francesca, Marco Beccuti, Chiara Fornari, et al.. (2013). Multi-level model for the investigation of oncoantigen-driven vaccination effect. BMC Bioinformatics. 14(S6). S11–S11. 9 indexed citations
19.
Beccuti, Marco, G. Franceschinis, & Serge Haddad. (2011). MDWNsolver: A Framework to Design and Solve Markov Decision Petri Nets. International Journal of Performability Engineering. 7(5). 417. 2 indexed citations
20.
Balbo, Gianfranco, et al.. (2010). Stochastic Petri Nets Sensitivity to Token Scheduling Policies.. RePEc: Research Papers in Economics. 181–186. 1 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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