Wayne Aubrey
Impact in
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- Scientific Computing and Data Management
- Health Informatics top 10%
Papers in
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- Genomics and Phylogenetic Studies 4
- Biomedical Text Mining and Ontologies 3
- Microbial Metabolic Engineering and Bioproduction 3
- Fungal and yeast genetics research 2
- RNA and protein synthesis mechanisms 2
- Gene expression and cancer classification 1
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- Scientific Computing and Data Management 2
- Co-authors
- Ross D. King (10 shared papers)Amanda Clare (11 shared papers)Larisa Soldatova (6 shared papers)Andrew C. Sparkes (8 shared papers)Michael J. Young (5 shared papers)Jem J. Rowland (5 shared papers)Stephen G. Oliver (6 shared papers)Maria Liakata (4 shared papers)
- Journals
- Bioinformatics (3 papers)PLoS ONE (2 papers)Computer (1 paper)Journal of The Royal Society Interface (1 paper)Open Biology (1 paper)
- Partner nations
- United KingdomBelgiumThailand
In The Last Decade
Wayne Aubrey
14 papers receiving 824 citations
Peers
Comparison fields: 5 of 136
- Information Systems and Management 83
- Health Informatics 12
- Computational Theory and Mathematics 129
- Biophysics 37
- Artificial Intelligence 179
Countries citing papers authored by Wayne Aubrey
This map shows the geographic impact of Wayne Aubrey'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 Wayne Aubrey with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Wayne Aubrey more than expected).
Fields of papers citing papers by Wayne Aubrey
This network shows the impact of papers produced by Wayne Aubrey. 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 Wayne Aubrey. The network helps show where Wayne Aubrey may publish in the future.
Co-authors
The 25 scholars most cited alongside Wayne Aubrey, 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 | 2009 | 474 | |
| 2 | 2010 | 120 | |
| 3 | 2015 | 114 | |
| 4 | 2008 | 33 | |
| 5 | 2009 | 32 | |
| 6 | 2021 | 30 | |
| 7 | 2013 | 29 | |
| 8 | 2020 | 13 | |
| 9 | 2011 | 7 | |
| 10 | 2009 | 5 | |
| 11 | 2013 | 5 | |
| 12 | 2019 | 1 | |
| 13 | 2023 | 1 | |
| 14 | Eve: Integration of machine learning with compound testing in a robot scientist | 2015 | 1 |
| 15 | 2015 | 0 |
About Wayne Aubrey
Wayne Aubrey is a scholar working on Molecular Biology, Information Systems and Management, Artificial Intelligence, Information Systems and Ecology, having authored 15 papers that have together received 865 indexed citations. Recurring topics across this work include Genomics and Phylogenetic Studies (4 papers), Biomedical Text Mining and Ontologies (3 papers), Microbial Metabolic Engineering and Bioproduction (3 papers), Fungal and yeast genetics research (2 papers), Scientific Computing and Data Management (2 papers), RNA and protein synthesis mechanisms (2 papers), Trypanosoma species research and implications (1 paper) and Gene expression and cancer classification (1 paper). The work is most often cited by research in Information Systems and Management (83 citations), Health Informatics (12 citations), Computational Theory and Mathematics (129 citations), Biophysics (37 citations) and Artificial Intelligence (179 citations). Wayne Aubrey has collaborated with scholars based in United Kingdom, Belgium and Thailand. Frequent co-authors include Ross D. King, Amanda Clare, Larisa Soldatova, Andrew C. Sparkes, Michael J. Young, Jem J. Rowland, Stephen G. Oliver, Maria Liakata, Emma Byrne and Kenneth E. Whelan. Their work appears in journals such as Bioinformatics, PLoS ONE, Computer, Journal of The Royal Society Interface and Open Biology.
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.