Fabio Rigat

22 papers receiving 579 citations

Peers

Fabio Rigat
Comparison fields: 5 of 94
  • Microbiology 152
  • Endocrine and Autonomic Systems 62
  • Epidemiology 261
  • Immunology 107
  • Cognitive Neuroscience 92
Replace Elisabeth Schuller with:
Elisabeth Schuller Austria
Zoila G. Rangel United States
Min Lin China
Michael DeVeer Australia
John Patrickson United States
Caitlin Russell United States
Eugénie E. Suter United States
Stanley Tam United States
R Rescaldani Italy
J Chodakewitz United States
Fabio Rigat relative to Elisabeth Schuller Austria Elisabeth Schuller's profile →
Citations per field
00.5×10.9×
Elisabeth Schuller · 1×
Citations per year

Countries citing papers authored by Fabio Rigat

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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.

Border = papers with Fabio Rigat Line = papers co-authored together Fabio Rigat links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 23 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2015162
2 2013120
3 200497
4 201652
5 201037
6 201436
7 201425
8 201819
9 201612
10 201112
11 20199
12 20113
13 20183
14 20093
15 20113
16 20202
17 20192
18 20192
19 20122
20
Antibodies to influenza nucleoprotein cross-react with human hypocretin receptor 2
20151

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.

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