Maria De Iorio
- Molecular Biology top 5%
- Genetics top 2%
- Artificial Intelligence top 2%
- Statistics and Probability top 0.5%
- Public Health, Environmental and Occupational Health top 5%
- Co-authors
- Timothy M. D. EbbelsPeter MüllerDavid J. BaldingJohn C. WhittakerClive HoggartElaine HolmesRobert GriffithsGary L. Rosner
- Topics
- Bayesian Methods and Mixture Models (35 papers)Statistical Methods and Bayesian Inference (24 papers)Statistical Methods and Inference (24 papers)
- Partner nations
- United KingdomSingaporeUnited States
In The Last Decade
Maria De Iorio
99 papers receiving 3.8k citations
Hit Papers
Peers
Comparison fields: 5 of 196
- Molecular Biology 1.7k
- Genetics 824
- Artificial Intelligence 586
- Statistics and Probability 547
- Public Health, Environmental and Occupational Health 372
Countries citing papers authored by Maria De Iorio
This map shows the geographic impact of Maria De Iorio'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 Maria De Iorio with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maria De Iorio more than expected).
Fields of papers citing papers by Maria De Iorio
This network shows the impact of papers produced by Maria De Iorio. 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 Maria De Iorio. The network helps show where Maria De Iorio may publish in the future.
Co-authorship network of co-authors of Maria De Iorio
This figure shows the co-authorship network connecting the top 25 collaborators of Maria De Iorio. A scholar is included among the top collaborators of Maria De Iorio 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 Maria De Iorio. Maria De Iorio is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 1 | |
| 4 | 2 | |
| 5 | 4 | |
| 6 | 3 | |
| 7 | 2 | |
| 8 | 4 | |
| 9 | 4 | |
| 10 | 2 | |
| 11 | 23 | |
| 12 | 14 | |
| 13 | 3 | |
| 14 | 2 | |
| 15 | 8 | |
| 16 | 42 | |
| 17 | 111 | |
| 18 | 54 | |
| 19 | 26 | |
| 20 | Discussion to the paper by Spiegelhalter et al., “Bayesian measures of complexity and fit” | 3 |
About Maria De Iorio
Maria De Iorio is a scholar working on Statistics and Probability, Artificial Intelligence and Genetics, having authored 108 papers that have together received 3.8k indexed citations. Recurring topics across this work include Bayesian Methods and Mixture Models (35 papers), Statistical Methods and Bayesian Inference (24 papers) and Statistical Methods and Inference (24 papers). The work is most often cited by research in Statistics and Probability (547 citations), Genetics (824 citations) and Molecular Biology (1.7k citations). Maria De Iorio has collaborated with scholars based in United Kingdom, Singapore and United States. Frequent co-authors include Timothy M. D. Ebbels, Peter Müller, David J. Balding, John C. Whittaker, Clive Hoggart, Elaine Holmes, Robert Griffiths, Gary L. Rosner, Jeremy K. Nicholson and Paul Elliott. Their work appears in journals such as Nature, Journal of the American Statistical Association and Bioinformatics.
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.