Robert Burbidge
Impact in
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- Computational Drug Discovery Methods
- Analytical Chemistry top 5%
- Spectroscopy and Chemometric Analyses
Papers in
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- Evolutionary Algorithms and Applications 2
- Metaheuristic Optimization Algorithms Research 2
- Reinforcement Learning in Robotics 2
- Machine Learning and Data Classification 1
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- Laser-Matter Interactions and Applications 2
- Advanced Fiber Laser Technologies 2
- Co-authors
- Bernard Buxton (1 shared paper)Matthew Trotter (1 shared paper)Sean B. Holden (1 shared paper)Benjamin J. Whitaker (2 shared papers)Ross D. King (1 shared paper)Jem J. Rowland (1 shared paper)
- Journals
- Information Sciences (1 paper)Journal of Modern Optics (1 paper)Research Explorer (The University of Manchester) (1 paper)Computers & Chemistry (1 paper)UCL Discovery (University College London) (1 paper)
- Partner nations
- United KingdomBelgium
In The Last Decade
Robert Burbidge
6 papers receiving 592 citations
Robert Burbidge's Hit Papers
Peers
Comparison fields: 5 of 126
- Computational Theory and Mathematics 295
- Analytical Chemistry 86
- Spectroscopy 87
- Molecular Biology 239
- Biophysics 18
Countries citing papers authored by Robert Burbidge
This map shows the geographic impact of Robert Burbidge'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 Robert Burbidge with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Robert Burbidge more than expected).
Fields of papers citing papers by Robert Burbidge
This network shows the impact of papers produced by Robert Burbidge. 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 Robert Burbidge. The network helps show where Robert Burbidge may publish in the future.
Co-authors
The 6 scholars most cited alongside Robert Burbidge, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Drug design by machine learning: support vector machines for pharmaceutical data analysis Hit paper breakdown → | 2001 | 542 |
| 2 | An introduction to support vector machines for data mining | 2001 | 53 |
| 3 | 2009 | 8 | |
| 4 | 2007 | 8 | |
| 5 | 2013 | 7 | |
| 6 | 2007 | 1 |
About Robert Burbidge
Robert Burbidge is a scholar working on Artificial Intelligence, Atomic and Molecular Physics, and Optics, Computational Theory and Mathematics, Computer Vision and Pattern Recognition and Spectroscopy, having authored 6 papers that have together received 619 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (2 papers), Evolutionary Algorithms and Applications (2 papers), Metaheuristic Optimization Algorithms Research (2 papers), Reinforcement Learning in Robotics (2 papers), Laser-Matter Interactions and Applications (2 papers), Advanced Fiber Laser Technologies (2 papers), Machine Learning and Data Classification (1 paper) and Face and Expression Recognition (1 paper). The work is most often cited by research in Computational Theory and Mathematics (295 citations), Analytical Chemistry (86 citations), Spectroscopy (87 citations), Molecular Biology (239 citations) and Biophysics (18 citations). Robert Burbidge has collaborated with scholars based in United Kingdom and Belgium. Frequent co-authors include Bernard Buxton, Matthew Trotter, Sean B. Holden, Benjamin J. Whitaker, Ross D. King and Jem J. Rowland. Their work appears in journals such as Information Sciences, Journal of Modern Optics, Research Explorer (The University of Manchester), Computers & Chemistry and UCL Discovery (University College London).
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.