Marina Vannucci
- Molecular Biology top 5%
- Artificial Intelligence top 1%
- Statistics and Probability top 0.5%
- Cognitive Neuroscience top 5%
- Genetics top 5%
- Co-authors
- Mahlet G. TadessePhilip J. BrownTom FearnNaijun ShaFrancesco C. StingoMichele GuindaniPíetro LióChristine B. Peterson
- Topics
- Bayesian Methods and Mixture Models (35 papers)Statistical Methods and Inference (27 papers)Gene expression and cancer classification (26 papers)
- Journals
- Proceedings of the National Academy of SciencesSHILAP Revista de lepidopterologíaJournal of the American Statistical Association
- Partner nations
- United StatesUnited KingdomItaly
In The Last Decade
Marina Vannucci
152 papers receiving 4.4k citations
Hit Papers
Peers
Comparison fields: 5 of 206
- Molecular Biology 1.6k
- Artificial Intelligence 1.1k
- Statistics and Probability 981
- Cognitive Neuroscience 488
- Genetics 441
Countries citing papers authored by Marina Vannucci
This map shows the geographic impact of Marina Vannucci'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 Marina Vannucci with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marina Vannucci more than expected).
Fields of papers citing papers by Marina Vannucci
This network shows the impact of papers produced by Marina Vannucci. 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 Marina Vannucci. The network helps show where Marina Vannucci may publish in the future.
Co-authorship network of co-authors of Marina Vannucci
This figure shows the co-authorship network connecting the top 25 collaborators of Marina Vannucci. A scholar is included among the top collaborators of Marina Vannucci 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 Marina Vannucci. Marina Vannucci is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | 5 | |
| 5 | 0 | |
| 6 | 1 | |
| 7 | 2 | |
| 8 | 10 | |
| 9 | Bayesian statistics and modellingbreakdown → | 616 |
| 10 | 16 | |
| 11 | 11 | |
| 12 | 4 | |
| 13 | 6 | |
| 14 | Statistical Analysis for High-Dimensional Data: The Abel Symposium 2014 | 3 |
| 15 | 30 | |
| 16 | 32 | |
| 17 | Incorporating biological information into linear models: a bayesian approach to the selection of pathways and genes | 95 |
| 18 | A Dirichlet process mixture of hidden Markov models for protein structure prediction | 14 |
| 19 | 2 | |
| 20 | 56 |
About Marina Vannucci
Marina Vannucci is a scholar working on Statistics and Probability, Computational Mathematics and Analytical Chemistry, having authored 162 papers that have together received 4.6k indexed citations. Recurring topics across this work include Bayesian Methods and Mixture Models (35 papers), Statistical Methods and Inference (27 papers) and Gene expression and cancer classification (26 papers). The work is most often cited by research in Statistics and Probability (981 citations), Artificial Intelligence (1.1k citations) and Analytical Chemistry (293 citations). Marina Vannucci has collaborated with scholars based in United States, United Kingdom and Italy. Frequent co-authors include Mahlet G. Tadesse, Philip J. Brown, Tom Fearn, Naijun Sha, Francesco C. Stingo, Michele Guindani, Píetro Lió, Christine B. Peterson, Andrew Gelman and Bianca Kramer. Their work appears in journals such as Proceedings of the National Academy of Sciences, SHILAP Revista de lepidopterología and Journal of the American Statistical Association.
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.