Charles Cox

2.1k total citations
19 papers, 1.3k citations indexed

About

Charles Cox is a scholar working on Molecular Biology, Immunology and Genetics. According to data from OpenAlex, Charles Cox has authored 19 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Molecular Biology, 6 papers in Immunology and 4 papers in Genetics. Recurrent topics in Charles Cox's work include Chronic Lymphocytic Leukemia Research (4 papers), Mycobacterium research and diagnosis (3 papers) and Monoclonal and Polyclonal Antibodies Research (3 papers). Charles Cox is often cited by papers focused on Chronic Lymphocytic Leukemia Research (4 papers), Mycobacterium research and diagnosis (3 papers) and Monoclonal and Polyclonal Antibodies Research (3 papers). Charles Cox collaborates with scholars based in United Kingdom, United States and Portugal. Charles Cox's co-authors include Matthew R. Nelson, Judong Shen, Alexander Dilthey, Gil McVean, Margaret G. Ehm, Xiuwen Zheng, J. C. Wakefield, B. S. Weir, Karen S. King and Colin F. Spraggs and has published in prestigious journals such as Nature Genetics, Journal of Clinical Oncology and Blood.

In The Last Decade

Charles Cox

19 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Charles Cox United Kingdom 17 328 283 246 241 198 19 1.3k
Surakameth Mahasirimongkol Thailand 26 327 1.0× 522 1.8× 226 0.9× 225 0.9× 210 1.1× 131 2.2k
N J Fitch United Kingdom 9 422 1.3× 269 1.0× 163 0.7× 361 1.5× 182 0.9× 14 2.1k
D. Sayer Australia 18 978 3.0× 245 0.9× 219 0.9× 180 0.7× 329 1.7× 52 2.6k
Lyoung Hyo Kim South Korea 28 388 1.2× 697 2.5× 437 1.8× 78 0.3× 131 0.7× 73 2.0k
Christine Lonjou United Kingdom 16 241 0.7× 457 1.6× 473 1.9× 99 0.4× 398 2.0× 26 2.0k
E P Sampaio Brazil 13 425 1.3× 662 2.3× 104 0.4× 111 0.5× 180 0.9× 25 2.4k
Ludger Leifeld Germany 26 528 1.6× 227 0.8× 182 0.7× 184 0.8× 143 0.7× 73 2.1k
Rui M. M. Victorino Portugal 25 1.2k 3.8× 203 0.7× 157 0.6× 431 1.8× 72 0.4× 102 2.6k
A. Martin Australia 19 783 2.4× 210 0.7× 75 0.3× 130 0.5× 239 1.2× 27 2.1k
Ullrich Schwertschlag United States 18 811 2.5× 323 1.1× 501 2.0× 51 0.2× 104 0.5× 50 1.9k

Countries citing papers authored by Charles Cox

Since Specialization
Citations

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

Fields of papers citing papers by Charles Cox

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Charles Cox

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

All Works

19 of 19 papers shown
1.
Wasik, Kaja A., Tomaz Berisa, Joseph K. Pickrell, et al.. (2021). Comparing low-pass sequencing and genotyping for trait mapping in pharmacogenetics. BMC Genomics. 22(1). 197–197. 32 indexed citations
2.
Dilthey, Alexander, Charles Cox, Zamin Iqbal, Matthew R. Nelson, & Gil McVean. (2015). Improved genome inference in the MHC using a population reference graph. Nature Genetics. 47(6). 682–688. 106 indexed citations
3.
Briley, Linda P., Judong Shen, Paul J. Newcombe, et al.. (2015). Comprehensive genome-wide evaluation of lapatinib-induced liver injury yields a single genetic signal centered on known risk allele HLA-DRB1*07:01. The Pharmacogenomics Journal. 16(2). 180–185. 45 indexed citations
4.
Niederer, Heather, et al.. (2015). Accurate interrogation of FCGR3A rs396991 in European and Asian populations using a widely available TaqMan genotyping method. Pharmacogenetics and Genomics. 25(11). 569–572. 4 indexed citations
5.
Schaid, Daniel J., Colin F. Spraggs, Shannon K. McDonnell, et al.. (2014). Prospective Validation of HLA-DRB1*07:01 Allele Carriage As a Predictive Risk Factor for Lapatinib-Induced Liver Injury. Journal of Clinical Oncology. 32(22). 2296–2303. 61 indexed citations
6.
Zheng, Xiuwen, Judong Shen, Charles Cox, et al.. (2013). HIBAG—HLA genotype imputation with attribute bagging. The Pharmacogenomics Journal. 14(2). 192–200. 245 indexed citations
7.
Dilthey, Alexander, Stephen Leslie, Loukas Moutsianas, et al.. (2013). Multi-Population Classical HLA Type Imputation. PLoS Computational Biology. 9(2). e1002877–e1002877. 125 indexed citations
8.
McHugh, Simon, Shilina Roman, Annelize Koch, et al.. (2012). Effects of genetic variation in the P2RX7 gene on pharmacodynamics of a P2X7 receptor antagonist: a prospective genotyping approach. British Journal of Clinical Pharmacology. 74(2). 376–380. 18 indexed citations
9.
Spraggs, Colin F., Linda P. Briley, Nan Bing, et al.. (2011). HLA-DQA1 * 02:01 Is a Major Risk Factor for Lapatinib-Induced Hepatotoxicity in Women With Advanced Breast Cancer. Journal of Clinical Oncology. 29(6). 667–673. 199 indexed citations
10.
Spraggs, Colin F., Linda P. Briley, Charles Cox, et al.. (2011). HLA-DRB1*07:01-DQA1*02:01 and UGT1A1*28 allele carriage in hepatic serious adverse event cases identified during lapatinib clinical trials.. Journal of Clinical Oncology. 29(15_suppl). 547–547. 1 indexed citations
11.
Kazeem, Gbenga, Charles Cox, Jennifer L. Aponte, et al.. (2009). High-resolution HLA genotyping and severe cutaneous adverse reactions in lamotrigine-treated patients. Pharmacogenetics and Genomics. 19(9). 661–665. 85 indexed citations
12.
Craigen, Jenny, Wendy J.M. Mackus, Sam R. Miller, et al.. (2009). Ofatumumab, a Human Mab Targeting a Membrane-Proximal Small-Loop Epitope On CD20, Induces Potent NK Cell-Mediated ADCC.. Blood. 114(22). 1725–1725. 20 indexed citations
13.
Hughes, Arlene R., William Spreen, Michael Mosteller, et al.. (2008). Pharmacogenetics of hypersensitivity to abacavir: from PGx hypothesis to confirmation to clinical utility. The Pharmacogenomics Journal. 8(6). 365–374. 61 indexed citations
14.
Stankovich, Jim, Charles Cox, Douglas S. Montgomery, et al.. (2006). On the utility of data from the International HapMap Project for Australian association studies. Human Genetics. 119(1-2). 220–222. 17 indexed citations
15.
Trégouët, David‐Alexandre, Ann Samnegård, Angharad R. Morgan, et al.. (2005). Haplotype Effect of the Matrix Metalloproteinase-1 Gene on Risk of Myocardial Infarction. Circulation Research. 97(10). 1070–1076. 65 indexed citations
16.
Cox, Charles, Karen E. Kempsell, & J. S. Hill Gaston. (2002). Investigation of infectious agents associated with arthritis by reverse transcription PCR of bacterial rRNA. Arthritis Research & Therapy. 5(1). R1–8. 39 indexed citations
17.
18.
Kempsell, Karen E., Charles Cox, Anthony Wong, et al.. (2000). Reverse Transcriptase-PCR Analysis of Bacterial rRNA for Detection and Characterization of Bacterial Species in Arthritis Synovial Tissue. Infection and Immunity. 68(10). 6012–6026. 94 indexed citations
19.
Gaston, Hill, Charles Cox, & Kaisa Granfors. (1999). Clinical and experimental evidence for persistentYersinia infection in reactive arthritis. Arthritis & Rheumatism. 42(10). 2239–2242. 67 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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