Fabio Rigat
Impact in
- Microbiology top 5%
- Bacterial Infections and Vaccines
- Endocrine and Autonomic Systems top 10%
Papers in
-
- Pneumonia and Respiratory Infections 5
-
- Gene Regulatory Network Analysis 2
- Co-authors
- Rino Rappuoli (3 shared papers)Jay Lucidarme (1 shared paper)Jamie Findlow (1 shared paper)Alessia Biolchi (1 shared paper)Mariagrazia Pizza (1 shared paper)Duccio Medini (1 shared paper)Marzia Monica Giuliani (1 shared paper)Ray Borrow (1 shared paper)
- Journals
- Vaccine (3 papers)Annals of Oncology (2 papers)BMC Medical Research Methodology (2 papers)Clinical Infectious Diseases (1 paper)Computational Statistics & Data Analysis (1 paper)
- Partner nations
- United KingdomItalyUnited States
In The Last Decade
Fabio Rigat
22 papers receiving 579 citations
Peers
Comparison fields: 5 of 94
- Microbiology 152
- Endocrine and Autonomic Systems 62
- Epidemiology 261
- Immunology 107
- Cognitive Neuroscience 92
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 23 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2015 | 162 | |
| 2 | 2013 | 120 | |
| 3 | 2004 | 97 | |
| 4 | 2016 | 52 | |
| 5 | 2010 | 37 | |
| 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 | 2009 | 3 | |
| 15 | 2011 | 3 | |
| 16 | 2020 | 2 | |
| 17 | 2019 | 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 Epidemiology, Molecular Biology, Cognitive Neuroscience, Artificial Intelligence and Statistics and Probability, having authored 23 papers that have together received 604 indexed citations. Recurring topics across this work include Pneumonia and Respiratory Infections (5 papers), Statistical Methods and Inference (3 papers), Streptococcal Infections and Treatments (3 papers), Neonatal and Maternal Infections (3 papers), Monoclonal and Polyclonal Antibodies Research (3 papers), Bayesian Methods and Mixture Models (3 papers), Neural dynamics and brain function (2 papers) and Gene Regulatory Network Analysis (2 papers). The work is most often cited by research in Microbiology (152 citations), Endocrine and Autonomic Systems (62 citations), Epidemiology (261 citations), Immunology (107 citations) and Cognitive Neuroscience (92 citations). Fabio Rigat has collaborated with scholars based in United Kingdom, Italy and United States. Frequent co-authors include Rino Rappuoli, Jay Lucidarme, Jamie Findlow, Alessia Biolchi, Mariagrazia Pizza, Duccio Medini, Marzia Monica Giuliani, Ray Borrow, Hanna Nohynek and Wayne Volkmuth. Their work appears in journals such as Vaccine, Annals of Oncology, BMC Medical Research Methodology, Clinical Infectious Diseases and Computational Statistics & Data Analysis.
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.