Sean B. Holden
- Computational Theory and Mathematics top 1%
- Molecular Biology
- Artificial Intelligence top 5%
- Spectroscopy top 10%
- Analytical Chemistry top 5%
- Co-authors
- Matthew TrotterRobert BurbidgeBernard BuxtonP. J. RaynerMartin AnthonyPeter HammondJudith AllansonRaoul C. M. Hennekam
- Topics
- Neural Networks and Applications (8 papers)Machine Learning and Algorithms (7 papers)Computational Drug Discovery Methods (5 papers)
- Partner nations
- United KingdomSouth SudanUnited States
In The Last Decade
Sean B. Holden
25 papers receiving 1.0k citations
Hit Papers
Peers
Comparison fields: 5 of 163
- Computational Theory and Mathematics 394
- Molecular Biology 352
- Artificial Intelligence 243
- Spectroscopy 131
- Analytical Chemistry 98
Countries citing papers authored by Sean B. Holden
This map shows the geographic impact of Sean B. Holden'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 Sean B. Holden with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sean B. Holden more than expected).
Fields of papers citing papers by Sean B. Holden
This network shows the impact of papers produced by Sean B. Holden. 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 Sean B. Holden. The network helps show where Sean B. Holden may publish in the future.
Co-authorship network of co-authors of Sean B. Holden
This figure shows the co-authorship network connecting the top 25 collaborators of Sean B. Holden. A scholar is included among the top collaborators of Sean B. Holden 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 Sean B. Holden. Sean B. Holden 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 | 8 | |
| 3 | 1 | |
| 4 | 15 | |
| 5 | 34 | |
| 6 | 1 | |
| 7 | Robust Regression with Twinned Gaussian Processes | 13 |
| 8 | The Generalized FITC Approximation | 42 |
| 9 | 181 | |
| 10 | 33 | |
| 11 | Drug design by machine learning: support vector machines for pharmaceutical data analysisbreakdown → | 542 |
| 12 | 36 | |
| 13 | 19 | |
| 14 | 6 | |
| 15 | 3 | |
| 16 | 28 | |
| 17 | 21 | |
| 18 | Quantifying Generalization in Linearly Weighted Neural Networks. | 11 |
| 19 | Complexity reduction in Volterra connectionist networks using a self-structuring LMS algorithm | 4 |
| 20 | 3 |
About Sean B. Holden
Sean B. Holden is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Computer Vision and Pattern Recognition, having authored 26 papers that have together received 1.1k indexed citations. Recurring topics across this work include Neural Networks and Applications (8 papers), Machine Learning and Algorithms (7 papers) and Computational Drug Discovery Methods (5 papers). The work is most often cited by research in Computational Theory and Mathematics (394 citations), Analytical Chemistry (98 citations) and Spectroscopy (131 citations). Sean B. Holden has collaborated with scholars based in United Kingdom, South Sudan and United States. Frequent co-authors include Matthew Trotter, Robert Burbidge, Bernard Buxton, P. J. Rayner, Martin Anthony, Peter Hammond, Judith Allanson, Raoul C. M. Hennekam, Kieran C. Murphy and Adam Shaw. Their work appears in journals such as Bioinformatics, PLoS Computational Biology and Neural Computation.
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.