Kevin Bryson
Impact in
- Molecular Biology top 1%
- Protein Structure and Dynamics
- RNA and protein synthesis mechanisms
- Machine Learning in Bioinformatics
- Genomics and Phylogenetic Studies
- vaccines and immunoinformatics approaches
- Aging top 5%
Papers in
- Aging 1
- Co-authors
- David T. JonesLiam J. McGuffinDaniel BuchanJonathan J. WardFederico MinneciJaspreet Singh SodhiBernard F. BuxtonAnna Lobley
- Journals
- Bioinformatics (6 papers)Proteins Structure Function and Bioinformatics (5 papers)Nucleic Acids Research (5 papers)BMC Bioinformatics (2 papers)Proceedings of the National Academy of Sciences (1 paper)
- Partner nations
- United KingdomUnited StatesItaly
In The Last Decade
Kevin Bryson
37 papers receiving 7.0k citations
Hit Papers
Peers
Comparison fields: 5 of 156
- Molecular Biology 5.4k
- Aging 68
- Microbiology 221
- Biotechnology 259
- Endocrinology 149
Countries citing papers authored by Kevin Bryson
This map shows the geographic impact of Kevin Bryson'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 Kevin Bryson with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kevin Bryson more than expected).
Fields of papers citing papers by Kevin Bryson
This network shows the impact of papers produced by Kevin Bryson. 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 Kevin Bryson. The network helps show where Kevin Bryson may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Kevin Bryson, 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 | 2024 | 1 | |
| 2 | 2023 | 2 | |
| 3 | 2021 | 29 | |
| 4 | 2019 | 2 | |
| 5 | 2017 | 189 | |
| 6 | 2016 | 35 | |
| 7 | 2013 | 71 | |
| 8 | Scalable web services for the PSIPRED Protein Analysis Workbench Hit paper breakdown → | 2013 | 1088 |
| 9 | 2010 | 4 | |
| 10 | 2010 | 295 | |
| 11 | 2007 | 52 | |
| 12 | 2006 | 18 | |
| 13 | 2006 | 74 | |
| 14 | 2005 | 61 | |
| 15 | 2004 | 107 | |
| 16 | From GeneWeaver to Agmial, in Network Tools and Applications in Biology | 2002 | 1 |
| 17 | 2001 | 32 | |
| 18 | The PSIPRED protein structure prediction server Hit paper breakdown → | 2000 | 3071 |
| 19 | 1999 | 25 | |
| 20 | 1999 | 123 |
About Kevin Bryson
Kevin Bryson is a scholar working on Biological Psychiatry, Aging, Molecular Biology, Biophysics and Behavioral Neuroscience, having authored 41 papers that have together received 7.1k indexed citations. Recurring topics across this work include Machine Learning in Bioinformatics (12 papers), Protein Structure and Dynamics (11 papers), Genomics and Phylogenetic Studies (11 papers), Enzyme Structure and Function (7 papers), Bioinformatics and Genomic Networks (7 papers), RNA and protein synthesis mechanisms (3 papers), Metabolomics and Mass Spectrometry Studies (2 papers) and Mitochondrial Function and Pathology (2 papers). The work is most often cited by research in Molecular Biology (5.4k citations), Aging (68 citations), Microbiology (221 citations), Biotechnology (259 citations) and Endocrinology (149 citations). Kevin Bryson has collaborated with scholars based in United Kingdom, United States and Italy. Frequent co-authors include David T. Jones, Liam J. McGuffin, Daniel Buchan, Jonathan J. Ward, Federico Minneci, Jaspreet Singh Sodhi, Bernard F. Buxton, Anna Lobley, Domenico Cozzetto and Caroline Hadley. Their work appears in journals such as Bioinformatics, Proteins Structure Function and Bioinformatics, Nucleic Acids Research, BMC Bioinformatics and Proceedings of the National Academy of Sciences.
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.