James Lyons
Impact in
- Molecular Biology top 5%
- Machine Learning in Bioinformatics
- Protein Structure and Dynamics
- RNA and protein synthesis mechanisms
- Genomics and Phylogenetic Studies
- vaccines and immunoinformatics approaches
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- Computational Drug Discovery Methods
Papers in
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- Machine Learning in Bioinformatics 24
- Protein Structure and Dynamics 19
- RNA and protein synthesis mechanisms 11
- Genomics and Phylogenetic Studies 10
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- Speech and Audio Processing 9
- Co-authors
- Kuldip K. Paliwal (32 shared papers)Abdollah Dehzangi (24 shared papers)Alok Sharma (23 shared papers)Rhys Heffernan (12 shared papers)Abdul Sattar (10 shared papers)Yaoqi Zhou (7 shared papers)Yuedong Yang (7 shared papers)Jihua Wang (3 shared papers)
In The Last Decade
James Lyons
38 papers receiving 1.7k citations
Peers
Comparison fields: 5 of 116
- Molecular Biology 1.5k
- Computational Theory and Mathematics 282
- Signal Processing 141
- Microbiology 31
- Biophysics 28
Countries citing papers authored by James Lyons
This map shows the geographic impact of James Lyons'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 James Lyons with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites James Lyons more than expected).
Fields of papers citing papers by James Lyons
This network shows the impact of papers produced by James Lyons. 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 James Lyons. The network helps show where James Lyons may publish in the future.
Co-authors
The 25 scholars most cited alongside James Lyons, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 39 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2015 | 270 | |
| 2 | 2014 | 222 | |
| 3 | 2016 | 132 | |
| 4 | 2012 | 130 | |
| 5 | 2014 | 118 | |
| 6 | 2018 | 81 | |
| 7 | 2015 | 76 | |
| 8 | 2015 | 69 | |
| 9 | 2014 | 63 | |
| 10 | 2014 | 63 | |
| 11 | 2018 | 52 | |
| 12 | 2008 | 47 | |
| 13 | 2013 | 43 | |
| 14 | 2014 | 39 | |
| 15 | 2010 | 39 | |
| 16 | 2008 | 36 | |
| 17 | 2015 | 34 | |
| 18 | 2015 | 30 | |
| 19 | 2016 | 28 | |
| 20 | 2014 | 27 |
About James Lyons
James Lyons is a scholar working on Molecular Biology, Signal Processing, Artificial Intelligence, Materials Chemistry and Computational Mechanics, having authored 39 papers that have together received 1.8k indexed citations. Recurring topics across this work include Machine Learning in Bioinformatics (24 papers), Protein Structure and Dynamics (19 papers), RNA and protein synthesis mechanisms (11 papers), Genomics and Phylogenetic Studies (10 papers), Speech and Audio Processing (9 papers), Speech Recognition and Synthesis (6 papers), Enzyme Structure and Function (5 papers) and Computational Drug Discovery Methods (4 papers). The work is most often cited by research in Molecular Biology (1.5k citations), Computational Theory and Mathematics (282 citations), Signal Processing (141 citations), Microbiology (31 citations) and Biophysics (28 citations). James Lyons has collaborated with scholars based in Australia, Fiji and Japan. Frequent co-authors include Kuldip K. Paliwal, Abdollah Dehzangi, Alok Sharma, Rhys Heffernan, Abdul Sattar, Yaoqi Zhou, Yuedong Yang, Jihua Wang, Kamil Wójcicki and Tatsuhiko Tsunoda. Their work appears in journals such as Journal of Theoretical Biology, BMC Bioinformatics, IEEE Transactions on NanoBioscience, Journal of Computational Chemistry and IEEE Signal Processing Letters.
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.