Jonathan Wagg

1.4k total citations
20 papers, 645 citations indexed

About

Jonathan Wagg is a scholar working on Molecular Biology, Oncology and Immunology. According to data from OpenAlex, Jonathan Wagg has authored 20 papers receiving a total of 645 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Molecular Biology, 7 papers in Oncology and 6 papers in Immunology. Recurrent topics in Jonathan Wagg's work include Immunotherapy and Immune Responses (5 papers), Cancer Immunotherapy and Biomarkers (3 papers) and vaccines and immunoinformatics approaches (3 papers). Jonathan Wagg is often cited by papers focused on Immunotherapy and Immune Responses (5 papers), Cancer Immunotherapy and Biomarkers (3 papers) and vaccines and immunoinformatics approaches (3 papers). Jonathan Wagg collaborates with scholars based in Switzerland, United Kingdom and United States. Jonathan Wagg's co-authors include Markus Krummenacker, Peter D. Karp, Michelle Green, Dale Kaiser, Suzanne Paley, Mark Coles, Eamonn A. Gaffney, Lucy Hutchinson, Alex Phipps and Benjamin Ribba and has published in prestigious journals such as Journal of Clinical Oncology, Blood and Scientific Reports.

In The Last Decade

Jonathan Wagg

19 papers receiving 621 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Jonathan Wagg Switzerland 8 480 82 65 46 46 20 645
Patroklos Samaras Germany 12 879 1.8× 97 1.2× 38 0.6× 48 1.0× 50 1.1× 15 1.1k
Alexander Mazein Luxembourg 13 636 1.3× 36 0.4× 70 1.1× 34 0.7× 34 0.7× 29 813
Ammar Ammar Netherlands 7 357 0.7× 52 0.6× 60 0.9× 42 0.9× 53 1.2× 19 621
Stephan Aiche Germany 8 758 1.6× 89 1.1× 28 0.4× 34 0.7× 48 1.0× 10 1.0k
Alok Jaiswal Finland 13 344 0.7× 108 1.3× 67 1.0× 56 1.2× 80 1.7× 30 622
Ting-Yi Sung Taiwan 19 847 1.8× 106 1.3× 56 0.9× 26 0.6× 29 0.6× 32 1.0k
Alberto Calderone Italy 12 521 1.1× 43 0.5× 101 1.6× 41 0.9× 41 0.9× 16 635
Lujia Chen United States 12 359 0.7× 85 1.0× 67 1.0× 72 1.6× 43 0.9× 36 603
Enrico Capobianco United States 13 306 0.6× 45 0.5× 39 0.6× 36 0.8× 51 1.1× 63 570
Jordi Carreras‐Puigvert Sweden 18 445 0.9× 172 2.1× 85 1.3× 25 0.5× 61 1.3× 33 750

Countries citing papers authored by Jonathan Wagg

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Jonathan Wagg

This figure shows the co-authorship network connecting the top 25 collaborators of Jonathan Wagg. A scholar is included among the top collaborators of Jonathan Wagg 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 Jonathan Wagg. Jonathan Wagg is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
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Wagg, Jonathan, et al.. (2022). De-risking clinical trial failure through mechanistic simulation. PubMed. 2(1). ltac017–ltac017. 4 indexed citations
3.
Gaffney, Eamonn A., et al.. (2021). Quantifying the limits of CAR T-cell delivery in mice and men. Journal of The Royal Society Interface. 18(176). 20201013–20201013. 20 indexed citations
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Olsson‐Brown, Anna, Rosemary Lord, Joseph J. Sacco, et al.. (2020). Two distinct clinical patterns of checkpoint inhibitor-induced thyroid dysfunction. Endocrine Connections. 9(4). 318–325. 23 indexed citations
6.
Gaffney, Eamonn A., et al.. (2019). Combining Mathematical Models With Experimentation to Drive Novel Mechanistic Insights Into Macrophage Function. Frontiers in Immunology. 10. 1283–1283. 11 indexed citations
7.
Gaffney, Eamonn A., et al.. (2018). Applications of mechanistic modelling to clinical and experimental immunology: an emerging technology to accelerate immunotherapeutic discovery and development. Clinical & Experimental Immunology. 193(3). 284–292. 7 indexed citations
8.
Gaffney, Eamonn A., et al.. (2018). An in silico model of cytotoxic T-lymphocyte activation in the lymph node following short peptide vaccination. Journal of The Royal Society Interface. 15(140). 20180041–20180041. 3 indexed citations
9.
Hutchinson, Lucy, Bernhard Steiert, Antoine Soubret, et al.. (2018). Models and Machines: How Deep Learning Will Take Clinical Pharmacology to the Next Level. CPT Pharmacometrics & Systems Pharmacology. 8(3). 131–134. 39 indexed citations
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Wagg, Jonathan, Oliver Krieter, Chia-Huey Ooi, et al.. (2017). Effect of molecular mechanisms mediating bevacizumab (BEV) and vanucizumab (VAN) on gastrointestinal perforation: Use of artificial neural networks for integrated data analysis.. Journal of Clinical Oncology. 35(15_suppl). e15108–e15108. 1 indexed citations
12.
Mehta, Rashmi, Michelle Green, Bela Patel, & Jonathan Wagg. (2016). Concentration-QT analysis of the randomized, placebo- and moxifloxacin-controlled thorough QT study of umeclidinium monotherapy and umeclidinium/vilanterol combination in healthy subjects. Journal of Pharmacokinetics and Pharmacodynamics. 43(2). 153–164. 5 indexed citations
13.
Hutchinson, Lucy, Eamonn A. Gaffney, Philip K. Maini, et al.. (2016). Vascular phenotype identification and anti-angiogenic treatment recommendation: A pseudo-multiscale mathematical model of angiogenesis. Journal of Theoretical Biology. 398. 162–180. 7 indexed citations
14.
Hutchinson, Lucy, Eamonn A. Gaffney, P. K. Maini, et al.. (2016). Modeling Longitudinal Preclinical Tumor Size Data to Identify Transient Dynamics in Tumor Response to Antiangiogenic Drugs. CPT Pharmacometrics & Systems Pharmacology. 5(11). 636–645. 14 indexed citations
15.
Stroh, Mark, David Carlile, Chi‐Chung Li, et al.. (2015). Challenges and Opportunities for Quantitative Clinical Pharmacology in Cancer Immunotherapy: Something Old, Something New, Something Borrowed, and Something Blue. CPT Pharmacometrics & Systems Pharmacology. 4(9). 495–497. 12 indexed citations
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Lyketsos, Constantine G., Susan Abushakra, Earvin Liang, et al.. (2012). P4‐324: Effects of oral ELND005 (scyllo‐inositol) on neuropsychiatric symptoms in a 78‐week phase 2 study in mild/moderate Alzheimer's disease (AD): Potential role of myo‐inositol reduction. Alzheimer s & Dementia. 8(4S_Part_21). 2 indexed citations
18.
Wagg, Jonathan, Michelle Green, Yan Li, et al.. (2011). P2‐509: Population pharmacokinetic analysis of plasma, cerebrospinal fluid and brain ELND005 in patients with mild to moderate Alzheimer's disease. Alzheimer s & Dementia. 7(4S_Part_13). 1 indexed citations
19.
Wagg, Jonathan, et al.. (2004). Computational prediction of human metabolic pathways from the complete human genome. Genome biology. 6(1). 371 indexed citations
20.
Karp, Peter D., Markus Krummenacker, Suzanne Paley, & Jonathan Wagg. (1999). Integrated pathway–genome databases and their role in drug discovery. Trends in biotechnology. 17(7). 275–281. 114 indexed citations

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.

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