Richard Dybowski

1.2k total citations
29 papers, 750 citations indexed

About

Richard Dybowski is a scholar working on Artificial Intelligence, Endocrinology and Food Science. According to data from OpenAlex, Richard Dybowski has authored 29 papers receiving a total of 750 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 6 papers in Endocrinology and 6 papers in Food Science. Recurrent topics in Richard Dybowski's work include Salmonella and Campylobacter epidemiology (5 papers), Vibrio bacteria research studies (5 papers) and Escherichia coli research studies (5 papers). Richard Dybowski is often cited by papers focused on Salmonella and Campylobacter epidemiology (5 papers), Vibrio bacteria research studies (5 papers) and Escherichia coli research studies (5 papers). Richard Dybowski collaborates with scholars based in United Kingdom, United States and Canada. Richard Dybowski's co-authors include Vanya Gant, Peter Weller, Dirk Husmeier, Stephen Roberts, Olivier Restif, Pietro Mastroeni, Andrew J. Grant, Duncan J. Maskell, Trevor Collins and James W. Larrick and has published in prestigious journals such as The Lancet, PLoS ONE and Antimicrobial Agents and Chemotherapy.

In The Last Decade

Richard Dybowski

27 papers receiving 715 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Richard Dybowski United Kingdom 11 226 123 101 56 53 29 750
Georgios Feretzakis Greece 16 160 0.7× 108 0.9× 94 0.9× 21 0.4× 34 0.6× 90 785
Frank De Smet Belgium 21 261 1.2× 628 5.1× 307 3.0× 23 0.4× 88 1.7× 46 1.9k
Juliane Siebourg‐Polster Switzerland 7 116 0.5× 212 1.7× 46 0.5× 15 0.3× 42 0.8× 16 789
Jyoti Soni India 10 246 1.1× 83 0.7× 111 1.1× 309 5.5× 45 0.8× 26 739
Yiming Zhang China 18 211 0.9× 112 0.9× 314 3.1× 30 0.5× 261 4.9× 68 1.1k
Mauro Giacomini Italy 21 136 0.6× 307 2.5× 179 1.8× 100 1.8× 272 5.1× 171 1.6k
Rizwan Qureshi Pakistan 18 165 0.7× 274 2.2× 26 0.3× 29 0.5× 32 0.6× 90 1.4k
Ruiyan Luo United States 18 166 0.7× 149 1.2× 191 1.9× 19 0.3× 233 4.4× 77 1.4k
Kaoru Shimada Japan 21 562 2.5× 129 1.0× 90 0.9× 9 0.2× 130 2.5× 163 1.5k
Dimitris Kalles Greece 17 396 1.8× 74 0.6× 58 0.6× 22 0.4× 26 0.5× 125 1.1k

Countries citing papers authored by Richard Dybowski

Since Specialization
Citations

This map shows the geographic impact of Richard Dybowski'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 Richard Dybowski with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Richard Dybowski more than expected).

Fields of papers citing papers by Richard Dybowski

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Richard Dybowski. 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 Richard Dybowski. The network helps show where Richard Dybowski may publish in the future.

Co-authorship network of co-authors of Richard Dybowski

This figure shows the co-authorship network connecting the top 25 collaborators of Richard Dybowski. A scholar is included among the top collaborators of Richard Dybowski 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 Richard Dybowski. Richard Dybowski is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Dybowski, Richard. (2020). Interpretable machine learning as a tool for scientific discovery in chemistry. New Journal of Chemistry. 44(48). 20914–20920. 42 indexed citations
2.
Dybowski, Richard, Olivier Restif, David J. Price, & Pietro Mastroeni. (2017). Inferring within-host bottleneck size: A Bayesian approach. Journal of Theoretical Biology. 435. 218–228. 1 indexed citations
3.
Price, David J., et al.. (2017). An efficient moments-based inference method for within-host bacterial infection dynamics. PLoS Computational Biology. 13(11). e1005841–e1005841. 7 indexed citations
4.
Dybowski, Richard, et al.. (2015). Single passage in mouse organs enhances the survival and spread of Salmonella enterica. Journal of The Royal Society Interface. 12(113). 20150702–20150702. 10 indexed citations
5.
Dybowski, Richard, et al.. (2014). Advanced QR Code Based Identity Card: A New Era for Generating Student ID Card in Developing Countries. International Conference on Systems. 97–103. 9 indexed citations
6.
Restif, Olivier, et al.. (2014). The Effects of Vaccination and Immunity on Bacterial Infection Dynamics In Vivo. PLoS Pathogens. 10(9). e1004359–e1004359. 27 indexed citations
7.
Dybowski, Richard, Trevelyan J. McKinley, Pietro Mastroeni, & Olivier Restif. (2013). Nested Sampling for Bayesian Model Comparison in the Context of Salmonella Disease Dynamics. PLoS ONE. 8(12). e82317–e82317. 8 indexed citations
8.
Dybowski, Richard, et al.. (2003). Introduction to the special issue on the fusion of domain knowledge with data for decision support. Journal of Machine Learning Research. 4. 293–294. 12 indexed citations
9.
Dybowski, Richard, et al.. (2001). Clinical Applications of Artificial Neural Networks. Cambridge University Press eBooks. 112 indexed citations
10.
Dybowski, Richard, et al.. (2001). Highly Epidemic Strains of Methicillin-Resistant Staphylococcus aureus Not Distinguished by Capsule Formation, Protein A Content or Adherence to HEp-2 Cells. European Journal of Clinical Microbiology & Infectious Diseases. 20(1). 27–32. 10 indexed citations
11.
Sebastiani, Paola, Richard Dybowski, & Marco Ramoni. (2001). Robust Outcome Prediction for Intensive-Care Patients. Methods of Information in Medicine. 40(1). 39–45. 10 indexed citations
12.
Dybowski, Richard. (1998). Classification of incomplete feature vectors by radial basis function networks. Pattern Recognition Letters. 19(14). 1257–1264. 10 indexed citations
13.
Dybowski, Richard, Trevor Collins, & Peter Weller. (1996). Visualization of binary string convergence by Sammon mapping. Open Research Online (The Open University). 377–383. 20 indexed citations
14.
Dybowski, Richard, et al.. (1996). Prediction of outcome in critically ill patients using artificial neural network synthesised by genetic algorithm. The Lancet. 347(9009). 1146–1150. 171 indexed citations
15.
Dybowski, Richard, et al.. (1996). Prediction of Outcome in the Critically Ill Using an Artificial Neural Network Synthesised By a Genetic Algorithm. 1 indexed citations
16.
Dybowski, Richard & Vanya Gant. (1995). Artificial neural networks in pathology and medical laboratories. The Lancet. 346(8984). 1203–1207. 96 indexed citations
17.
Pearson, Anita, et al.. (1995). Tuberculosis in Inner London: evidence for an increase in young adults and immigrants. Epidemiology and Infection. 115(1). 133–137. 3 indexed citations
18.
Dybowski, Richard, W. R. Gransden, & Ian Phillips. (1993). Towards a statistically oriented decision support system for the management of septicaemia. Artificial Intelligence in Medicine. 5(6). 489–502. 4 indexed citations
19.
Ayscough, P. B., et al.. (1988). ChemInform Abstract: Some Developments in Expert Systems in Chemistry. ChemInform. 19(1). 4 indexed citations
20.
Dybowski, Richard & Norman Taylor. (1988). Towards a steroid-profiling expert system. Chemometrics and Intelligent Laboratory Systems. 5(1). 65–72. 1 indexed citations

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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