Jim Kaufman

11.5k total citations · 2 hit papers
139 papers, 8.1k citations indexed

About

Jim Kaufman is a scholar working on Immunology, Molecular Biology and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Jim Kaufman has authored 139 papers receiving a total of 8.1k indexed citations (citations by other indexed papers that have themselves been cited), including 111 papers in Immunology, 34 papers in Molecular Biology and 24 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Jim Kaufman's work include T-cell and B-cell Immunology (90 papers), Immune Cell Function and Interaction (60 papers) and Immunotherapy and Immune Responses (38 papers). Jim Kaufman is often cited by papers focused on T-cell and B-cell Immunology (90 papers), Immune Cell Function and Interaction (60 papers) and Immunotherapy and Immune Responses (38 papers). Jim Kaufman collaborates with scholars based in United Kingdom, United States and Switzerland. Jim Kaufman's co-authors include J L Strominger, Alan J. Korman, Martin F. Flajnik, Jan Salomonsen, Jack L. Strominger, Deborah A. Shackelford, Stephan Beck, Louis Du Pasquier, Hans‐Joachim Wallny and Sarah Milne and has published in prestigious journals such as Nature, Cell and Proceedings of the National Academy of Sciences.

In The Last Decade

Jim Kaufman

135 papers receiving 7.8k citations

Hit Papers

Mice lacking MHC class II molecules 1984 2026 1998 2012 1991 1984 250 500 750

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jim Kaufman United Kingdom 47 5.6k 2.1k 1.0k 946 942 139 8.1k
Martin F. Flajnik United States 59 7.2k 1.3× 3.6k 1.7× 572 0.6× 263 0.3× 2.3k 2.4× 174 10.2k
Robert G. Urban United States 22 5.1k 0.9× 2.0k 1.0× 779 0.8× 855 0.9× 1.4k 1.5× 33 7.0k
R. M. E. Parkhouse United Kingdom 55 3.6k 0.7× 2.3k 1.1× 498 0.5× 664 0.7× 1.5k 1.6× 327 11.0k
L. William Clem United States 52 6.6k 1.2× 1.8k 0.8× 565 0.6× 168 0.2× 1.1k 1.1× 197 8.4k
Gregory W. Warr United States 45 5.2k 0.9× 2.0k 0.9× 533 0.5× 184 0.2× 863 0.9× 174 7.2k
Claude–Agnès Reynaud France 48 4.9k 0.9× 3.4k 1.6× 829 0.8× 559 0.6× 1.5k 1.6× 112 8.0k
Steven Kessler United States 23 1.9k 0.3× 2.6k 1.2× 1.4k 1.3× 668 0.7× 974 1.0× 50 5.7k
Arsène Burny Belgium 63 6.4k 1.1× 4.3k 2.0× 1.4k 1.4× 1.4k 1.5× 368 0.4× 333 12.3k
Reinhold Schirmbeck Germany 45 4.0k 0.7× 1.9k 0.9× 764 0.8× 1.9k 2.0× 272 0.3× 152 6.4k
Jean–Claude Weill France 43 4.0k 0.7× 2.9k 1.4× 686 0.7× 479 0.5× 1.1k 1.2× 101 6.9k

Countries citing papers authored by Jim Kaufman

Since Specialization
Citations

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

Fields of papers citing papers by Jim Kaufman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jim Kaufman

This figure shows the co-authorship network connecting the top 25 collaborators of Jim Kaufman. A scholar is included among the top collaborators of Jim Kaufman 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 Jim Kaufman. Jim Kaufman 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
1.
Kaiser, Michael G., Jim Kaufman, & Susan J. Lamont. (2024). Different MHC class I cell surface expression levels in diverse chicken lines, associations with B blood group, and proposed relationship to antigen-binding repertoire. Poultry Science. 104(1). 104569–104569. 3 indexed citations
4.
Kaufman, Jim, et al.. (2022). New vistas unfold: Chicken MHC molecules reveal unexpected ways to present peptides to the immune system. Frontiers in Immunology. 13. 886672–886672. 5 indexed citations
5.
Tregaskes, Clive A. & Jim Kaufman. (2021). Chickens as a simple system for scientific discovery: The example of the MHC. Molecular Immunology. 135. 12–20. 13 indexed citations
6.
Hamede, Rodrigo, Thomas Madsen, Hamish McCallum, et al.. (2020). Darwin, the devil, and the management of transmissible cancers. Conservation Biology. 35(2). 748–751. 13 indexed citations
7.
Bichet, Coraline, Édouard Guitton, Andrew P. Krupa, et al.. (2019). Development and optimization of a hybridization technique to type the classical class I and class II B genes of the chicken MHC. Immunogenetics. 71(10). 647–663. 4 indexed citations
8.
Tovar, Cesar, Ruth J. Pye, Alexandre Kreiss, et al.. (2017). Regression of devil facial tumour disease following immunotherapy in immunised Tasmanian devils. Scientific Reports. 7(1). 43827–43827. 44 indexed citations
9.
Berry, Richard, Stephen J. Headey, Melissa Call, et al.. (2014). Structure of the Chicken CD3ϵδ/γ Heterodimer and Its Assembly with the αβT Cell Receptor. Journal of Biological Chemistry. 289(12). 8240–8251. 13 indexed citations
10.
Walker, Brian A., Lawrence Hunt, Karsten Skjødt, et al.. (2011). The dominantly expressed class I molecule of the chicken MHC is explained by coevolution with the polymorphic peptide transporter (TAP) genes. Proceedings of the National Academy of Sciences. 108(20). 8396–8401. 69 indexed citations
11.
Shaw, Iain, Timothy J. Powell, Denise A. Marston, et al.. (2007). Different Evolutionary Histories of the Two Classical Class I Genes BF1 and BF2 Illustrate Drift and Selection within the Stable MHC Haplotypes of Chickens. The Journal of Immunology. 178(9). 5744–5752. 69 indexed citations
12.
Salomonsen, Jan, Maria Rathmann Sørensen, Denise A. Marston, et al.. (2005). Two CD1 genes map to the chicken MHC, indicating that CD1 genes are ancient and likely to have been present in the primordial MHC. Proceedings of the National Academy of Sciences. 102(24). 8668–8673. 87 indexed citations
13.
Rogers, Sally, Thomas Göbel, Birgit C. Viertlboeck, et al.. (2005). Characterization of the Chicken C-Type Lectin-Like Receptors B-NK and B-lec Suggests That the NK Complex and the MHC Share a Common Ancestral Region. The Journal of Immunology. 174(6). 3475–3483. 67 indexed citations
14.
Kaiser, Pete, Tuang Yeow Poh, Lisa Rothwell, et al.. (2005). A Genomic Analysis of Chicken Cytokines and Chemokines. Journal of Interferon & Cytokine Research. 25(8). 467–484. 179 indexed citations
16.
Kaufman, Jim. (1990). Evolution of the MHC: Lessons from the nonmammalian vertebrates. Immunologic Research. 9(2). 123–134.
17.
Kaufman, Jim, Karsten Skjoedt, & Jan Salomonsen. (1990). The MHC Molecules of Nonmammalian Vertebrates. Immunological Reviews. 113(1). 83–117. 55 indexed citations
18.
Kaufman, Jim, et al.. (1990). MHC-like molecules in some nonmammalian vertebrates can be detected by some cross-reactive monoclonal antibodies.. The Journal of Immunology. 144(6). 2273–2280. 25 indexed citations
19.
Kaufman, Jim, Martin F. Flajnik, Louis Du Pasquier, & Patricia Riegert. (1985). Xenopus MHC class II molecules. I. Identification and structural characterization.. The Journal of Immunology. 134(5). 3248–3257. 61 indexed citations
20.
Fuks, A, et al.. (1977). Structural aspects of the products of the human major histocompatibility complex.. Munich Personal RePEc Archive (Ludwig Maximilian University of Munich). 9(4). 1685–9. 3 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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