Jonathan Wagg
Impact in
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- Bioinformatics and Genomic Networks
- Microbial Metabolic Engineering and Bioproduction
- Metabolomics and Mass Spectrometry Studies
- Gene Regulatory Network Analysis
- Gene expression and cancer classification
- Genomics and Phylogenetic Studies
- Biomedical Text Mining and Ontologies
- Modeling and Simulation top 10%
Papers in ⓘ
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- Mathematical Biology Tumor Growth 2
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- Immunotherapy and Immune Responses 5
- Immune Cell Function and Interaction 2
- Co-authors
- Markus Krummenacker (2 shared papers)Peter D. Karp (2 shared papers)Michelle Green (3 shared papers)Dale Kaiser (1 shared paper)Suzanne Paley (1 shared paper)Mark Coles (7 shared papers)Eamonn A. Gaffney (8 shared papers)Lucy Hutchinson (3 shared papers)
- Journals
- Alzheimer s & Dementia (3 papers)CPT Pharmacometrics & Systems Pharmacology (3 papers)Scientific Reports (2 papers)Journal of The Royal Society Interface (2 papers)Trends in biotechnology (1 paper)
- Partner nations
- SwitzerlandUnited KingdomUnited States
In The Last Decade
Jonathan Wagg
19 papers receiving 621 citations
Peers
Comparison fields: 5 of 91
- Molecular Biology 480
- Modeling and Simulation 19
- Computational Theory and Mathematics 65
- Oncology 82
- Cancer Research 44
Countries citing papers authored by Jonathan Wagg
This map shows the geographic impact of Jonathan Wagg'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 Jonathan Wagg with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jonathan Wagg more than expected).
Fields of papers citing papers by Jonathan Wagg
This network shows the impact of papers produced by Jonathan Wagg. 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 Jonathan Wagg. The network helps show where Jonathan Wagg may publish in the future.
Co-authors
The 25 scholars most cited alongside Jonathan Wagg, 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 | 2004 | 371 | |
| 2 | 1999 | 114 | |
| 3 | 2018 | 39 | |
| 4 | 2020 | 23 | |
| 5 | 2021 | 20 | |
| 6 | 2016 | 14 | |
| 7 | 2015 | 12 | |
| 8 | 2019 | 11 | |
| 9 | 2016 | 7 | |
| 10 | 2018 | 7 | |
| 11 | 2017 | 6 | |
| 12 | 2016 | 5 | |
| 13 | 2022 | 4 | |
| 14 | 2023 | 3 | |
| 15 | 2018 | 3 | |
| 16 | 2021 | 2 | |
| 17 | 2012 | 2 | |
| 18 | 2017 | 1 | |
| 19 | 2011 | 1 | |
| 20 | 2012 | 0 |
About Jonathan Wagg
Jonathan Wagg is a scholar working on Modeling and Simulation, Immunology, Oncology, Genetics and Radiology, Nuclear Medicine and Imaging, having authored 20 papers that have together received 645 indexed citations. Recurring topics across this work include Immunotherapy and Immune Responses (5 papers), Cancer Immunotherapy and Biomarkers (3 papers), vaccines and immunoinformatics approaches (3 papers), Monoclonal and Polyclonal Antibodies Research (3 papers), Microbial Metabolic Engineering and Bioproduction (2 papers), Immune Cell Function and Interaction (2 papers), Angiogenesis and VEGF in Cancer (2 papers) and Mathematical Biology Tumor Growth (2 papers). The work is most often cited by research in Molecular Biology (480 citations), Modeling and Simulation (19 citations), Computational Theory and Mathematics (65 citations), Oncology (82 citations) and Cancer Research (44 citations). Jonathan Wagg has collaborated with scholars based in Switzerland, United Kingdom and United States. Frequent co-authors include Markus Krummenacker, Peter D. Karp, Michelle Green, Dale Kaiser, Suzanne Paley, Mark Coles, Eamonn A. Gaffney, Lucy Hutchinson, Benjamin Ribba and Alex Phipps. Their work appears in journals such as Alzheimer s & Dementia, CPT Pharmacometrics & Systems Pharmacology, Scientific Reports, Journal of The Royal Society Interface and Trends in biotechnology.
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.