Jake E McGreig
Impact in
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- Computational Drug Discovery Methods
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- SARS-CoV-2 and COVID-19 Research
- COVID-19 Clinical Research Studies
Papers in
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- Protein Structure and Dynamics 3
- Bioinformatics and Genomic Networks 2
- Machine Learning in Bioinformatics 1
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- Computational Drug Discovery Methods 2
- Co-authors
- Mark N. Wass (7 shared papers)Martin Michaelis (4 shared papers)Magdalena Antczak (2 shared papers)Michael J.E. Sternberg (1 shared paper)Sandra Ciesek (2 shared papers)Christian Muench (1 shared paper)Kevin Klann (1 shared paper)Peter Braubach (1 shared paper)
- Journals
- Nucleic Acids Research (3 papers)Bioinformatics (2 papers)Protein Science (1 paper)PLoS Biology (1 paper)The Journal of Infectious Diseases (1 paper)
- Partner nations
- United KingdomGermanyUnited States
In The Last Decade
Jake E McGreig
10 papers receiving 296 citations
Peers
Comparison fields: 5 of 72
- Computational Theory and Mathematics 66
- Infectious Diseases 63
- Molecular Biology 184
- Structural Biology 2
- Applied Microbiology and Biotechnology 2
Countries citing papers authored by Jake E McGreig
This map shows the geographic impact of Jake E McGreig'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 Jake E McGreig with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jake E McGreig more than expected).
Fields of papers citing papers by Jake E McGreig
This network shows the impact of papers produced by Jake E McGreig. 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 Jake E McGreig. The network helps show where Jake E McGreig may publish in the future.
Co-authors
The 25 scholars most cited alongside Jake E McGreig, 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 | 2019 | 94 | |
| 2 | 2021 | 65 | |
| 3 | 2020 | 63 | |
| 4 | 2022 | 36 | |
| 5 | 2019 | 13 | |
| 6 | 2021 | 12 | |
| 7 | 2022 | 12 | |
| 8 | 2021 | 5 | |
| 9 | 2024 | 5 | |
| 10 | 2024 | 1 |
About Jake E McGreig
Jake E McGreig is a scholar working on Molecular Biology, Computational Theory and Mathematics, Infectious Diseases, Molecular Medicine and Pharmacology, having authored 10 papers that have together received 306 indexed citations. Recurring topics across this work include Protein Structure and Dynamics (3 papers), Bioinformatics and Genomic Networks (2 papers), Computational Drug Discovery Methods (2 papers), Enzyme Structure and Function (2 papers), COVID-19 Clinical Research Studies (1 paper), Cardiovascular Effects of Exercise (1 paper), Machine Learning in Bioinformatics (1 paper) and Advanced Proteomics Techniques and Applications (1 paper). The work is most often cited by research in Computational Theory and Mathematics (66 citations), Infectious Diseases (63 citations), Molecular Biology (184 citations), Structural Biology (2 citations) and Applied Microbiology and Biotechnology (2 citations). Jake E McGreig has collaborated with scholars based in United Kingdom, Germany and United States. Frequent co-authors include Mark N. Wass, Martin Michaelis, Magdalena Antczak, Michael J.E. Sternberg, Sandra Ciesek, Christian Muench, Kevin Klann, Peter Braubach, Danny David Jonigk and Jindrich Jr. Cinatl. Their work appears in journals such as Nucleic Acids Research, Bioinformatics, Protein Science, PLoS Biology and The Journal of Infectious Diseases.
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.