Peter Parham

63.0k total citations · 14 hit papers
534 papers, 44.0k citations indexed

About

Peter Parham is a scholar working on Immunology, Molecular Biology and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Peter Parham has authored 534 papers receiving a total of 44.0k indexed citations (citations by other indexed papers that have themselves been cited), including 438 papers in Immunology, 78 papers in Molecular Biology and 52 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Peter Parham's work include T-cell and B-cell Immunology (385 papers), Immune Cell Function and Interaction (344 papers) and Immunotherapy and Immune Responses (136 papers). Peter Parham is often cited by papers focused on T-cell and B-cell Immunology (385 papers), Immune Cell Function and Interaction (344 papers) and Immunotherapy and Immune Responses (136 papers). Peter Parham collaborates with scholars based in United States, United Kingdom and France. Peter Parham's co-authors include Steven G. E. Marsh, Lisbeth A. Guethlein, Lewis L. Lanier, James Robinson, Jenny E. Gumperz, Carlos Vilches, Jacqueline Zemmour, Frances M. Brodsky, Walter F. Bodmer and Joseph H. Phillips and has published in prestigious journals such as Nature, Science and Cell.

In The Last Decade

Peter Parham

527 papers receiving 42.7k citations

Hit Papers

The IPD and IMGT/HLA... 1979 2026 1994 2010 2014 2005 1997 2002 1979 400 800 1.2k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Peter Parham United States 114 36.7k 5.9k 4.8k 3.3k 3.2k 534 44.0k
John Trowsdale United Kingdom 101 23.8k 0.6× 8.8k 1.5× 1.9k 0.4× 2.0k 0.6× 3.6k 1.1× 428 34.1k
Max D. Cooper United States 99 23.4k 0.6× 7.0k 1.2× 3.1k 0.6× 5.6k 1.7× 2.6k 0.8× 450 33.6k
Ellis L. Reinherz United States 96 24.2k 0.7× 9.2k 1.6× 2.0k 0.4× 11.1k 3.3× 5.5k 1.7× 392 36.0k
Michael B. Brenner United States 103 27.8k 0.8× 9.9k 1.7× 1.2k 0.3× 2.9k 0.9× 5.7k 1.8× 295 40.8k
Pamela J. Björkman United States 79 14.6k 0.4× 9.7k 1.6× 3.3k 0.7× 7.4k 2.2× 1.8k 0.5× 245 29.0k
Edward A. Clark United States 86 15.3k 0.4× 5.9k 1.0× 1.2k 0.2× 3.4k 1.0× 3.1k 1.0× 354 23.0k
Eric O. Long United States 80 17.6k 0.5× 4.5k 0.8× 1.3k 0.3× 1.7k 0.5× 3.4k 1.0× 202 22.1k
Marie‐Paule Lefranc France 72 11.8k 0.3× 9.1k 1.6× 1.6k 0.3× 7.2k 2.1× 2.2k 0.7× 480 20.9k
Walter F. Bodmer United Kingdom 105 12.6k 0.3× 13.8k 2.3× 2.1k 0.4× 4.8k 1.4× 8.6k 2.7× 565 42.1k
John F. Kearney United States 69 11.9k 0.3× 6.4k 1.1× 1.3k 0.3× 4.7k 1.4× 1.9k 0.6× 264 21.0k

Countries citing papers authored by Peter Parham

Since Specialization
Citations

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

Fields of papers citing papers by Peter Parham

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Peter Parham

This figure shows the co-authorship network connecting the top 25 collaborators of Peter Parham. A scholar is included among the top collaborators of Peter Parham 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 Peter Parham. Peter Parham 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.
Terry, James P., James Goff, Kruawun Jankaew, et al.. (2024). Elevation and age of a raised beach in the upper Gulf of Thailand, as evidence for regional sea level during the Late Holocene. Journal of Asian Earth Sciences. 273. 106259–106259.
2.
Houwaart, Torsten, Stephan Scholz, William Palmer, et al.. (2023). Complete sequences of six major histocompatibility complex haplotypes, including all the major MHC class II structures. HLA. 102(1). 28–43. 14 indexed citations
3.
Wroblewski, Emily E., Lisbeth A. Guethlein, Weimin Liu, et al.. (2023). Malaria-driven adaptation of MHC class I in wild bonobo populations. Nature Communications. 14(1). 1033–1033. 3 indexed citations
4.
Legrand, Nolwenn, Patrice Chevallier, Gaëlle David, et al.. (2023). Non-Expressed Donor KIR3DL1 Alleles May Represent a Risk Factor for Relapse after T-Replete Haploidentical Hematopoietic Stem Cell Transplantation. Cancers. 15(10). 2754–2754. 2 indexed citations
5.
Switzer, Adam D., Jędrzej Majewski, Ella Meilianda, et al.. (2019). The tsunami deposits of the September 28, 2018 Palu earthquake, Sulawesi, Indonesia.. EGU General Assembly Conference Abstracts. 6290.
6.
Nemat‐Gorgani, Neda, et al.. (2019). In vitro education of human natural killer cells by KIR3DL1. Life Science Alliance. 2(6). e201900434–e201900434. 7 indexed citations
7.
Nemat‐Gorgani, Neda, Lisbeth A. Guethlein, Brenna M. Henn, et al.. (2019). Diversity of KIR, HLA Class I, and Their Interactions in Seven Populations of Sub-Saharan Africans. The Journal of Immunology. 202(9). 2636–2647. 20 indexed citations
9.
Cooley, Sarah, Peter Parham, & Jeffrey S. Miller. (2018). Strategies to activate NK cells to prevent relapse and induce remission following hematopoietic stem cell transplantation. Blood. 131(10). 1053–1062. 97 indexed citations
10.
Henn, Brenna M., Christopher R. Gignoux, Matthew J. Jobin, et al.. (2011). Hunter-gatherer genomic diversity suggests a southern African origin for modern humans. Proceedings of the National Academy of Sciences. 108(13). 5154–5162. 260 indexed citations breakdown →
11.
Aguilar, Anastazia M. Older, Lisbeth A. Guethlein, Laurent Abi-Rached, & Peter Parham. (2011). Natural variation at position 45 in the D1 domain of lineage III killer cell immunoglobulin-like receptors (KIR) has major effects on the avidity and specificity for MHC class I. Immunogenetics. 63(8). 543–547. 10 indexed citations
12.
Aguilar, Anastazia M. Older, Lisbeth A. Guethlein, Erin J. Adams, et al.. (2010). Coevolution of Killer Cell Ig-Like Receptors with HLA-C To Become the Major Variable Regulators of Human NK Cells. The Journal of Immunology. 185(7). 4238–4251. 65 indexed citations
14.
Robinson, James, et al.. (2003). IMGT/HLA and IMGT/MHC – sequence databases for the study of the major histocompatibility complex.. UCL Discovery (University College London). 3 indexed citations
15.
Parham, Peter. (2001). Regulatory T cells. Munksgaard eBooks. 6 indexed citations
16.
Parham, Peter. (1999). Genomic organisation of the MHC : structure, origin and function. Munksgaard eBooks. 7 indexed citations
17.
Marsh, Steven G. E., Ronald E. Bontrop, Wolfgang R. Mayr, et al.. (1997). Nomenclature for factors of the HLA system, 1996. European Journal of Immunogenetics. 24(2). 105–151. 48 indexed citations
18.
Little, Ann‐Margaret, et al.. (1996). HLA‐C typing of eleven Papua New Guineans: identification of an HLA‐Cw4/Cw2 hybrid allele. Tissue Antigens. 48(2). 113–117. 12 indexed citations
19.
Arnett, Kelly L., Erin J. Adams, Jenny E. Gumperz, et al.. (1995). Expression of an unusual Bw4 epitope by a subtype of HLA‐B8 [B*0802]. Tissue Antigens. 46(4). 316–321. 13 indexed citations
20.
Little, Ann‐Margaret & Peter Parham. (1991). The HLA‐Bw75 subtype of B15: Molecular characterization and comparison with crossreacting antigens. Tissue Antigens. 38(4). 186–190. 26 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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