Fabio Rigat
Impact in
- Microbiology top 5%
- Bacterial Infections and Vaccines
- Endocrine and Autonomic Systems top 10%
Papers in
-
- Bayesian Methods and Mixture Models 3
-
- Statistical Methods and Inference 3
- Markov Chains and Monte Carlo Methods 2
- Co-authors
- Rino Rappuoli (3 shared papers)Jamie Findlow (1 shared paper)Jay Lucidarme (1 shared paper)Duccio Medini (1 shared paper)Marzia Monica Giuliani (1 shared paper)Mariagrazia Pizza (1 shared paper)Alessia Biolchi (1 shared paper)Ray Borrow (1 shared paper)
- Journals
- Vaccine (3 papers)BMC Medical Research Methodology (2 papers)Annals of Oncology (2 papers)Statistics in Medicine (1 paper)Blood (1 paper)
- Partner nations
- United KingdomItalyUnited States
In The Last Decade
Fabio Rigat
22 papers receiving 586 citations
Peers
Comparison fields: 5 of 88
- Microbiology 149
- Endocrine and Autonomic Systems 47
- Epidemiology 192
- Cognitive Neuroscience 93
- Immunology 97
Countries citing papers authored by Fabio Rigat
This map shows the geographic impact of Fabio Rigat'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 Fabio Rigat with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fabio Rigat more than expected).
Fields of papers citing papers by Fabio Rigat
This network shows the impact of papers produced by Fabio Rigat. 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 Fabio Rigat. The network helps show where Fabio Rigat may publish in the future.
Co-authors
The 25 scholars most cited alongside Fabio Rigat, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 25 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2015 | 165 | |
| 2 | 2013 | 120 | |
| 3 | 2004 | 97 | |
| 4 | 2016 | 53 | |
| 5 | 2010 | 38 | |
| 6 | 2014 | 36 | |
| 7 | 2014 | 25 | |
| 8 | 2018 | 19 | |
| 9 | 2016 | 12 | |
| 10 | 2011 | 12 | |
| 11 | 2019 | 9 | |
| 12 | 2011 | 3 | |
| 13 | 2018 | 3 | |
| 14 | 2011 | 3 | |
| 15 | 2009 | 3 | |
| 16 | 2019 | 2 | |
| 17 | 2020 | 2 | |
| 18 | 2019 | 2 | |
| 19 | 2012 | 2 | |
| 20 | Antibodies to influenza nucleoprotein cross-react with human hypocretin receptor 2 | 2015 | 1 |
About Fabio Rigat
Fabio Rigat is a scholar working on Artificial Intelligence, Statistics and Probability, Cognitive Neuroscience, Epidemiology and Molecular Biology, having authored 25 papers that have together received 609 indexed citations. Recurring topics across this work include Monoclonal and Polyclonal Antibodies Research (3 papers), Statistical Methods and Inference (3 papers), Neonatal and Maternal Infections (3 papers), Bayesian Methods and Mixture Models (3 papers), Markov Chains and Monte Carlo Methods (2 papers), Bacterial Infections and Vaccines (2 papers), Pneumonia and Respiratory Infections (2 papers) and Health Systems, Economic Evaluations, Quality of Life (2 papers). The work is most often cited by research in Microbiology (149 citations), Endocrine and Autonomic Systems (47 citations), Epidemiology (192 citations), Cognitive Neuroscience (93 citations) and Immunology (97 citations). Fabio Rigat has collaborated with scholars based in United Kingdom, Italy and United States. Frequent co-authors include Rino Rappuoli, Jamie Findlow, Jay Lucidarme, Duccio Medini, Marzia Monica Giuliani, Mariagrazia Pizza, Alessia Biolchi, Ray Borrow, Hanna Nohynek and Outi Vaarala. Their work appears in journals such as Vaccine, BMC Medical Research Methodology, Annals of Oncology, Statistics in Medicine and Blood.
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.