Joe G. Greener
- Molecular Biology top 10%
- Computational Theory and Mathematics top 2%
- Materials Chemistry
- Artificial Intelligence top 10%
- Biomedical Engineering
- Co-authors
- David T. JonesShaun M. KandathilLewis MoffatMichael J.E. SternbergIoannis FilippisAndy M. LauEdward W. TateAlan Armstrong
- Topics
- Protein Structure and Dynamics (14 papers)Machine Learning in Bioinformatics (7 papers)Computational Drug Discovery Methods (4 papers)
- Journals
- Proceedings of the National Academy of SciencesAngewandte Chemie International EditionNature Communications
- Partner nations
- United KingdomIndiaChina
In The Last Decade
Joe G. Greener
19 papers receiving 1.9k citations
Hit Papers
Peers
Comparison fields: 5 of 169
- Molecular Biology 989
- Computational Theory and Mathematics 251
- Materials Chemistry 249
- Artificial Intelligence 156
- Biomedical Engineering 147
Countries citing papers authored by Joe G. Greener
This map shows the geographic impact of Joe G. Greener'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 Joe G. Greener with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Joe G. Greener more than expected).
Fields of papers citing papers by Joe G. Greener
This network shows the impact of papers produced by Joe G. Greener. 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 Joe G. Greener. The network helps show where Joe G. Greener may publish in the future.
Co-authorship network of co-authors of Joe G. Greener
This figure shows the co-authorship network connecting the top 25 collaborators of Joe G. Greener. A scholar is included among the top collaborators of Joe G. Greener 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 Joe G. Greener. Joe G. Greener is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 10 | |
| 2 | 1 | |
| 3 | 1 | |
| 4 | 2 | |
| 5 | 19 | |
| 6 | 33 | |
| 7 | A guide to machine learning for biologistsbreakdown → | 1220 |
| 8 | 21 | |
| 9 | 8 | |
| 10 | 129 | |
| 11 | 38 | |
| 12 | 77 | |
| 13 | DMPfold: fast de novo protein model generation from covarying sequences using predicted distances and iterative model building | 1 |
| 14 | 76 | |
| 15 | 56 | |
| 16 | 6 | |
| 17 | 35 | |
| 18 | 73 | |
| 19 | 94 |
About Joe G. Greener
Joe G. Greener is a scholar working on Structural Biology, Molecular Biology and Computational Theory and Mathematics, having authored 19 papers that have together received 1.9k indexed citations. Recurring topics across this work include Protein Structure and Dynamics (14 papers), Machine Learning in Bioinformatics (7 papers) and Computational Drug Discovery Methods (4 papers). The work is most often cited by research in Health Informatics (40 citations), Computational Theory and Mathematics (251 citations) and Molecular Biology (989 citations). Joe G. Greener has collaborated with scholars based in United Kingdom, India and China. Frequent co-authors include David T. Jones, Shaun M. Kandathil, Lewis Moffat, Michael J.E. Sternberg, Ioannis Filippis, Andy M. Lau, Edward W. Tate, Alan Armstrong, Gregory B. Craven and Dominic P. Affron. Their work appears in journals such as Proceedings of the National Academy of Sciences, Angewandte Chemie International Edition and Nature Communications.
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.