Marco Antoniotti

2.2k total citations
78 papers, 888 citations indexed

About

Marco Antoniotti is a scholar working on Molecular Biology, Cancer Research and Artificial Intelligence. According to data from OpenAlex, Marco Antoniotti has authored 78 papers receiving a total of 888 indexed citations (citations by other indexed papers that have themselves been cited), including 45 papers in Molecular Biology, 17 papers in Cancer Research and 15 papers in Artificial Intelligence. Recurrent topics in Marco Antoniotti's work include Bioinformatics and Genomic Networks (19 papers), Gene Regulatory Network Analysis (18 papers) and Cancer Genomics and Diagnostics (15 papers). Marco Antoniotti is often cited by papers focused on Bioinformatics and Genomic Networks (19 papers), Gene Regulatory Network Analysis (18 papers) and Cancer Genomics and Diagnostics (15 papers). Marco Antoniotti collaborates with scholars based in Italy, United States and United Kingdom. Marco Antoniotti's co-authors include Bud Mishra, Alex Graudenzi, Giancarlo Mauri, Giulio Caravagna, Daniele Ramazzotti, Alberto Policriti, Davide Maspero, Giovanni De Matteis, Fabrizio Angaroni and Loes M. Olde Loohuis and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Marco Antoniotti

75 papers receiving 863 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 Antoniotti Italy 17 482 211 149 122 98 78 888
Marco Beccuti Italy 20 535 1.1× 262 1.2× 146 1.0× 54 0.4× 43 0.4× 98 1.5k
Paul S. Andrews United Kingdom 17 526 1.1× 41 0.2× 140 0.9× 164 1.3× 78 0.8× 52 1.1k
Monika Heiner Germany 24 1.9k 3.9× 219 1.0× 375 2.5× 55 0.5× 95 1.0× 113 2.5k
Shulin Zhou China 23 470 1.0× 347 1.6× 569 3.8× 26 0.2× 27 0.3× 105 1.7k
Xikui Liu China 18 473 1.0× 159 0.8× 152 1.0× 70 0.6× 85 0.9× 77 1.4k
Daniele Ramazzotti Italy 17 813 1.7× 380 1.8× 34 0.2× 179 1.5× 137 1.4× 54 1.3k
Borja Calvo Spain 15 637 1.3× 217 1.0× 123 0.8× 380 3.1× 72 0.7× 40 1.4k
Shankar Vembu United States 15 343 0.7× 351 1.7× 65 0.4× 366 3.0× 131 1.3× 24 1.5k
Fatemeh Vafaee Australia 16 761 1.6× 429 2.0× 115 0.8× 119 1.0× 35 0.4× 66 1.2k

Countries citing papers authored by Marco Antoniotti

Since Specialization
Citations

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

Fields of papers citing papers by Marco Antoniotti

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Marco Antoniotti

This figure shows the co-authorship network connecting the top 25 collaborators of Marco Antoniotti. A scholar is included among the top collaborators of Marco Antoniotti 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 Antoniotti. Marco Antoniotti 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.
Giannese, Francesca, Oronza A. Botrugno, Chiara Balestrieri, et al.. (2024). Scalable integration of multiomic single-cell data using generative adversarial networks. Bioinformatics. 40(5). 1 indexed citations
2.
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
3.
d’Onofrio, Alberto, et al.. (2023). A Bayesian method to infer copy number clones from single-cell RNA and ATAC sequencing. PLoS Computational Biology. 19(11). e1011557–e1011557. 2 indexed citations
4.
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
5.
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
6.
Graudenzi, Alex, Davide Maspero, Marzia Di Filippo, et al.. (2018). Integration of transcriptomic data and metabolic networks in cancer samples reveals highly significant prognostic power. Journal of Biomedical Informatics. 87. 37–49. 19 indexed citations
7.
Ramazzotti, Daniele, Marco S. Nobile, Marco Antoniotti, & Alex Graudenzi. (2017). Learning the Probabilistic Structure of Cumulative Phenomena with Suppes-Bayes Causal Networks.. arXiv (Cornell University). 2 indexed citations
8.
Caravagna, Giulio, Alex Graudenzi, Daniele Ramazzotti, et al.. (2016). Algorithmic methods to infer the evolutionary trajectories in cancer progression. Proceedings of the National Academy of Sciences. 113(28). E4025–34. 48 indexed citations
9.
Luca, De, Giulio Caravagna, Daniele Ramazzotti, et al.. (2016). TRONCO: an R package for the inference of cancer progression models from heterogeneous genomic data. Bioinformatics. 32(12). 1911–1913. 21 indexed citations
10.
Ramazzotti, Daniele, Giulio Caravagna, Loes M. Olde Loohuis, et al.. (2015). CAPRI: efficient inference of cancer progression models from cross-sectional data. Bioinformatics. 31(18). 3016–3026. 57 indexed citations
11.
Caravagna, Giulio, Alberto d’Onofrio, Marco Antoniotti, & Giancarlo Mauri. (2014). Stochastic Hybrid Automata with delayed transitions to model biochemical systems with delays. Information and Computation. 236. 19–34. 4 indexed citations
12.
Loohuis, Loes M. Olde, Giulio Caravagna, Alex Graudenzi, et al.. (2014). Inferring Tree Causal Models of Cancer Progression with Probability Raising. PLoS ONE. 9(10). e108358–e108358. 37 indexed citations
13.
Cava, Claudia, Italo Zoppis, Manuela Gariboldi, et al.. (2014). Combined analysis of chromosomal instabilities and gene expression for colon cancer progression inference. PubMed. 4(1). 2–2. 11 indexed citations
14.
Loohuis, Loes M. Olde, Giulio Caravagna, Alex Graudenzi, et al.. (2013). Inferring causal models of cancer progression with a shrinkage estimator and probability raising. arXiv (Cornell University). 1 indexed citations
15.
Graudenzi, Alex, Giulio Caravagna, Giovanni De Matteis, Giancarlo Mauri, & Marco Antoniotti. (2012). A multiscale model of intestinal crypts dynamics. BOA (University of Milano-Bicocca). 1–13. 3 indexed citations
16.
Zoppis, Italo, Erica Gianazza, Clizia Chinello, et al.. (2011). Mutual Information Optimization for Mass Spectra Data Alignment. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 9(3). 934–939. 5 indexed citations
17.
Kleinberg, Samantha, Marco Antoniotti, Naren Ramakrishnan, & Bharat Mishra. (2007). Modal Logic, Temporal Models and Neural Circuits: What Connects Them. BOA (University of Milano-Bicocca). 1 indexed citations
18.
Mathew, Jomol, Barry S. Taylor, Gary D. Bader, et al.. (2007). From Bytes to Bedside: Data Integration and Computational Biology for Translational Cancer Research. PLoS Computational Biology. 3(2). e12–e12. 45 indexed citations
19.
20.
Antoniotti, Marco, Carla Piazza, Alberto Policriti, Marta Simeoni, & Bud Mishra. (2004). Taming the complexity of biochemical models through bisimulation and collapsing: theory and practice. Theoretical Computer Science. 325(1). 45–67. 27 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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