David Passmore

1.2k citations
24 papers · 724 · h-index 15

Impact in

Papers in

David Passmore

24 papers receiving 676 citations

Peers

David Passmore
Comparison fields: 5 of 64
  • Biotechnology 208
  • Radiology, Nuclear Medicine and Imaging 322
  • Immunology 293
  • Molecular Biology 424
  • Oncology 156
Replace Gordana Wozniak‐Knopp with:
Gordana Wozniak‐Knopp Austria
Kiran Khandke United States
Tiezheng Li United States
A.J. Cumber United Kingdom
Maria‐Ana Ghetie United States
Jinbiao Zhan China
Ragupathy Madiyalakan United States
Patrick Hossler United States
Susan L. Bernhard United States
Bénédicte Samyn-Petit France
David Passmore relative to Gordana Wozniak‐Knopp Austria Gordana Wozniak‐Knopp's profile →
Citations per field
00.5×1.5×2.3×
Gordana Wozniak‐Knopp · 1×
Citations per year

Countries citing papers authored by David Passmore

Since Specialization
Citations

This map shows the geographic impact of David Passmore'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 David Passmore with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Passmore more than expected).

Fields of papers citing papers by David Passmore

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David Passmore. 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 David Passmore. The network helps show where David Passmore may publish in the future.

Co-authors

The 25 scholars most cited alongside David Passmore, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with David Passmore Line = papers co-authored together David Passmore links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 24 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2006298
2 201549
3 200939
4 199535
5 201930
6 199329
7 199327
8 202026
9 201825
10 201525
11 199222
12 200421
13 201521
14 200921
15 199219
16 199210
17 19958
18 19946
19 20105
20 20102

About David Passmore

David Passmore is a scholar working on Radiology, Nuclear Medicine and Imaging, Molecular Biology, Immunology, Oncology and Organic Chemistry, having authored 24 papers that have together received 724 indexed citations. Recurring topics across this work include Monoclonal and Polyclonal Antibodies Research (15 papers), HER2/EGFR in Cancer Research (7 papers), Glycosylation and Glycoproteins Research (6 papers), Immunotherapy and Immune Responses (6 papers), T-cell and B-cell Immunology (5 papers), Computational Drug Discovery Methods (3 papers), Radiopharmaceutical Chemistry and Applications (3 papers) and Biosimilars and Bioanalytical Methods (2 papers). The work is most often cited by research in Biotechnology (208 citations), Radiology, Nuclear Medicine and Imaging (322 citations), Immunology (293 citations), Molecular Biology (424 citations) and Oncology (156 citations). David Passmore has collaborated with scholars based in United States, Sweden and Germany. Frequent co-authors include Pina M. Cardarelli, Bishwajit Nag, M. S. Srinivasan, Amelia Black, John R. Gasdaska, Jason D. Sterling, Lynn F. Dickey, Vangipuram S. Rangan, Brian R. Clark and Suresh D. Sharma. Their work appears in journals such as Cancer Research, The Journal of Immunology, Journal of Immunological Methods, Journal of the American Society for Mass Spectrometry and Clinical Cancer Research.

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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