James Hayhurst

10.7k total citations · 3 hit papers
13 papers, 2.0k citations indexed

About

James Hayhurst is a scholar working on Immunology, Molecular Biology and Genetics. According to data from OpenAlex, James Hayhurst has authored 13 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Immunology, 7 papers in Molecular Biology and 5 papers in Genetics. Recurrent topics in James Hayhurst's work include T-cell and B-cell Immunology (6 papers), Genetic Associations and Epidemiology (4 papers) and Immune Cell Function and Interaction (4 papers). James Hayhurst is often cited by papers focused on T-cell and B-cell Immunology (6 papers), Genetic Associations and Epidemiology (4 papers) and Immune Cell Function and Interaction (4 papers). James Hayhurst collaborates with scholars based in United Kingdom, United States and Germany. James Hayhurst's co-authors include Steven G. E. Marsh, James Robinson, Peter Parham, Jason A. Halliwell, Paul Flicek, Jeremy Schwartzentruber, Annalisa Buniello, Miguel Carmona, Maya Ghoussaini and Ian Dunham and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and Nature Genetics.

In The Last Decade

James Hayhurst

13 papers receiving 1.9k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

The IPD and IMGT/HLA database: allele variant databases

2014 1.4k citations James Robinson, Jason A. Halliwell et al. Nucleic Acids Research profile →
An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci

2021 223 citations Edward Mountjoy, Ellen M. Schmidt et al. Nature Genetics profile →
A compendium of uniformly processed human gene expression and splicing quantitative trait loci

2021 165 citations Nurlan Kerimov, James Hayhurst et al. Nature Genetics profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
James Hayhurst United Kingdom	9	1.4k	500	299	137	123	13	2.0k
Vikki Rand United Kingdom	15	562 0.4×	649 1.3×	217 0.7×	92 0.7×	164 1.3×	25	1.6k
Bruno Lucas France	32	2.3k 1.7×	443 0.9×	287 1.0×	114 0.8×	87 0.7×	73	2.9k
S Sawada Japan	15	1.1k 0.8×	626 1.3×	124 0.4×	106 0.8×	69 0.6×	17	1.7k
Maryam Yassai United States	20	1.5k 1.1×	250 0.5×	159 0.5×	165 1.2×	352 2.9×	49	2.0k
Thandi M. Onami United States	15	1.2k 0.9×	591 1.2×	252 0.8×	292 2.1×	140 1.1×	21	2.0k
Encarnita Mariotti‐Ferrandiz France	18	1.0k 0.7×	317 0.6×	119 0.4×	66 0.5×	37 0.3×	42	1.4k
Ingrid Faé Austria	19	396 0.3×	308 0.6×	161 0.5×	64 0.5×	207 1.7×	70	1.1k
Hiroshi Kosaka Japan	19	1.9k 1.4×	328 0.7×	291 1.0×	131 1.0×	124 1.0×	39	2.3k
Mark A. Kroenke United States	14	1.8k 1.3×	291 0.6×	81 0.3×	134 1.0×	61 0.5×	20	2.3k
Robert S. Wildin United States	19	2.1k 1.5×	589 1.2×	794 2.7×	120 0.9×	107 0.9×	31	3.0k

Countries citing papers authored by James Hayhurst

Since Specialization

Citations

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

Fields of papers citing papers by James Hayhurst

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of James Hayhurst

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

All Works

Sort: Min cites: Since: Top N: Style:

13 of 13 papers shown

Lynall, Mary-Ellen, Blagoje Soskic, James Hayhurst, et al.. (2022). Genetic variants associated with psychiatric disorders are enriched at epigenetically active sites in lymphoid cells. Nature Communications. 13(1). 6102–6102. 18 indexed citations

Mountjoy, Edward, Ellen M. Schmidt, Miguel Carmona, et al.. (2021). An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci. Nature Genetics. 53(11). 1527–1533. 223 indexed citations breakdown →

Kerimov, Nurlan, James Hayhurst, Jonathan Manning, et al.. (2021). A compendium of uniformly processed human gene expression and splicing quantitative trait loci. Nature Genetics. 53(9). 1290–1299. 165 indexed citations breakdown →

MacArthur, Jacqueline A. L., Annalisa Buniello, Laura W. Harris, et al.. (2021). Workshop proceedings: GWAS summary statistics standards and sharing. Cell Genomics. 1(1). 100004–100004. 17 indexed citations

Shilts, Jarrod, Jeremy Schwartzentruber, James Hayhurst, et al.. (2021). A proteome-wide genetic investigation identifies several SARS-CoV-2-exploited host targets of clinical relevance. eLife. 10. 21 indexed citations

Hayhurst, James, et al.. (2020). Single molecule real‐time DNA sequencing of the full HLA‐E gene for 212 reference cell lines. HLA. 95(6). 561–572. 6 indexed citations

Sollis, Elliot, Annalisa Buniello, María Cerezo, et al.. (2019). User-focused development of the NHGRI-EBI Genome-Wide Association Studies Catalog. Faculty of 1000 Research Ltd. 8. 1 indexed citations

Turner, Thomas R., James Hayhurst, Will P. Bultitude, et al.. (2017). Single molecule real‐time DNA sequencing of HLA genes at ultra‐high resolution from 126 International HLA and Immunogenetics Workshop cell lines. HLA. 91(2). 88–101. 41 indexed citations

Surendranath, Vineeth, James Hayhurst, Bianca Schöne, et al.. (2017). TypeLoader: A fast and efficient automated workflow for the annotation and submission of novel full‐length HLA alleles. HLA. 90(1). 25–31. 8 indexed citations

10.

Robinson, James, et al.. (2016). The IPD-IMGT/HLA Database – New developments in reporting HLA variation. Human Immunology. 77(3). 233–237. 84 indexed citations

11.

Watt, Jonathan, Simon Kennedy, Nadeem Ahmed, et al.. (2016). The relationship between oxidised LDL, endothelial progenitor cells and coronary endothelial function in patients with CHD. Open Heart. 3(1). e000342–e000342. 13 indexed citations

12.

Hayhurst, James, et al.. (2015). Two novel HLA alleles, HLA‐A*30:02:01:03 and HLA‐C*08:113, identified in a South African bone marrow donor. Tissue Antigens. 85(4). 291–293. 5 indexed citations

13.

Robinson, James, Jason A. Halliwell, James Hayhurst, et al.. (2014). The IPD and IMGT/HLA database: allele variant databases. Nucleic Acids Research. 43(D1). D423–D431. 1397 indexed citations breakdown →

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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