Kevin Barton

5.2k total citations · 1 hit paper
50 papers, 3.2k citations indexed

About

Kevin Barton is a scholar working on Molecular Biology, Hematology and Genetics. According to data from OpenAlex, Kevin Barton has authored 50 papers receiving a total of 3.2k indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Molecular Biology, 17 papers in Hematology and 16 papers in Genetics. Recurrent topics in Kevin Barton's work include Acute Myeloid Leukemia Research (10 papers), Multiple Myeloma Research and Treatments (7 papers) and Glioma Diagnosis and Treatment (7 papers). Kevin Barton is often cited by papers focused on Acute Myeloid Leukemia Research (10 papers), Multiple Myeloma Research and Treatments (7 papers) and Glioma Diagnosis and Treatment (7 papers). Kevin Barton collaborates with scholars based in United States, Canada and China. Kevin Barton's co-authors include Jeffrey M. Leiden, Natarajan Muthusamy, Chao-Nan Ting, Min Lü, Cynthia Clendenin, Christopher Fischer, Emily Barr, Margaret Veselits, Chay T. Kuo and Serhan Alkan and has published in prestigious journals such as Nature, New England Journal of Medicine and Journal of Clinical Investigation.

In The Last Decade

Kevin Barton

47 papers receiving 3.2k citations

Hit Papers

Transcription factor GATA-3 is required for development o... 1996 2026 2006 2016 1996 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kevin Barton United States 21 1.8k 1.0k 722 521 384 50 3.2k
Jeffrey L. Kutok United States 16 1.8k 1.0× 1.2k 1.2× 640 0.9× 735 1.4× 1.1k 2.8× 26 3.6k
Maria Luisa Sulis United States 19 2.2k 1.2× 399 0.4× 638 0.9× 764 1.5× 367 1.0× 52 3.2k
Mats Ehinger Sweden 30 1.5k 0.9× 426 0.4× 847 1.2× 796 1.5× 267 0.7× 86 3.2k
Stephan Teglund Sweden 15 2.0k 1.1× 1.1k 1.1× 1.8k 2.5× 386 0.7× 366 1.0× 23 3.9k
Gerwin Huls Netherlands 19 1.8k 1.0× 377 0.4× 844 1.2× 312 0.6× 200 0.5× 59 2.8k
Norihiko Kawamata Japan 38 2.2k 1.3× 467 0.5× 1.4k 1.9× 745 1.4× 588 1.5× 96 4.0k
Mark Lupher United States 27 1.6k 0.9× 1.3k 1.3× 596 0.8× 226 0.4× 218 0.6× 38 3.4k
G Vairo Australia 29 1.5k 0.9× 926 0.9× 1.3k 1.8× 476 0.9× 335 0.9× 49 2.8k
Silke Brüderlein Germany 30 1.5k 0.8× 746 0.7× 870 1.2× 152 0.3× 532 1.4× 65 3.5k
Olivier Albagli France 26 1.7k 1.0× 486 0.5× 361 0.5× 291 0.6× 213 0.6× 60 2.6k

Countries citing papers authored by Kevin Barton

Since Specialization
Citations

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

Fields of papers citing papers by Kevin Barton

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kevin Barton

This figure shows the co-authorship network connecting the top 25 collaborators of Kevin Barton. A scholar is included among the top collaborators of Kevin Barton 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 Kevin Barton. Kevin Barton 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.
Wang, Xiaoxin X., Kevin Barton, Komuraiah Myakala, et al.. (2025). Role of nuclear receptors, lipid metabolism, and mitochondrial function in the pathogenesis of diabetic kidney disease. American Journal of Physiology-Renal Physiology. 329(4). F510–F547. 3 indexed citations
2.
Myakala, Komuraiah, Xiaoxin X. Wang, Nataliia V. Shults, et al.. (2025). The nonsteroidal MR antagonist finerenone reverses Western diet-induced kidney disease by regulating mitochondrial and lipid metabolism and inflammation. American Journal of Physiology-Renal Physiology. 329(5). F724–F743.
3.
Huang, Jonathan, Miri Kim, Kristen L. Lauing, et al.. (2024). Age-stratified comorbid and pharmacologic analysis of patients with glioblastoma. Brain Behavior & Immunity - Health. 38. 100753–100753. 4 indexed citations
4.
5.
Goyal, Abhishek, Vikram C. Prabhu, Anupama Chundury, et al.. (2023). PATH-04. TRANSFORMATION OF GRADE 2 OLIGODENDROGLIOMA TO GLIOSARCOMA WITH RAPID AND EXTENSIVE PROGRESSION: CASE REPORT.. Neuro-Oncology. 25(Supplement_5). v167–v167.
6.
Lee, Jonathan, Rimas V. Lukas, Ignacio Jusué-Torres, et al.. (2023). MGMT Methylated High Grade Glioma with Distant Recurrence and Stable Original Tumor Site: Case Series. PubMed. 10(2). 2 indexed citations
7.
Berg, Stephanie, Lei Zhang, Shanhui Liu, et al.. (2023). Active STAT3 Prevents Selinexor-Induced Caspase-Independent Apoptosis in Multiple Myeloma. Blood. 142(Supplement 1). 6611–6611. 1 indexed citations
8.
Li, Yanchun, Christopher S. Seet, Kevin Barton, et al.. (2023). Distinct roles of hematopoietic cytokines in the regulation of leukemia stem cells in murine MLL-AF9 leukemia. Stem Cell Reports. 19(1). 100–111. 2 indexed citations
9.
Zhang, Jiwang, et al.. (2022). High-risk disease in newly diagnosed multiple myeloma: beyond the R-ISS and IMWG definitions. Blood Cancer Journal. 12(5). 83–83. 41 indexed citations
10.
Rodby, Roger A., David Cimbaluk, Parameswaran Venugopal, et al.. (2019). When Monoclonal Gammopathy is of Renal Significance: A Case Study of Crystalglobulinemia From Chicago Multiple Myeloma Rounds. Clinical Lymphoma Myeloma & Leukemia. 19(6). e251–e258. 7 indexed citations
11.
Bahar, Burak, Kevin Barton, & Ameet R. Kini. (2016). The role of the Exon 13 G571S JAK2 mutation in myeloproliferative neoplasms. Leukemia Research Reports. 6. 27–28. 8 indexed citations
13.
Garcia, Tracey A., Rima M. Dafer, Sara E. Hocker, et al.. (2007). Recurrent Strokes in Two Patients With POEMS Syndrome and Castleman’s Disease. Journal of Stroke and Cerebrovascular Diseases. 16(6). 278–284. 18 indexed citations
14.
Geng, Yanbiao, Peter Laslo, Kevin Barton, & Chyung‐Ru Wang. (2005). Transcriptional Regulation of CD1D1 by Ets Family Transcription Factors. The Journal of Immunology. 175(2). 1022–1029. 32 indexed citations
15.
Ni, Hongyu, Melek Ergin, Qin Huang, et al.. (2001). Analysis of expression of nuclear factor κB (NF‐κB) in multiple myeloma: downregulation of NF‐κB induces apoptosis. British Journal of Haematology. 115(2). 279–286. 135 indexed citations
16.
Hettmann, Thore, Kevin Barton, & Jeffrey M. Leiden. (2000). Microphthalmia due to p53-mediated apoptosis of anterior lens epithelial cells in mice lacking the CREB-2 transcription factor. Developmental Biology. 222(1). 110–123. 117 indexed citations
17.
Sood, Rashmi, et al.. (1999). Forced expression of the leukemia-associated gene EVI1 in ES cells: a model for myeloid leukemia with 3q26 rearrangements. Leukemia. 13(11). 1639–1645. 60 indexed citations
18.
Barton, Kevin, Natarajan Muthusamy, Christopher Fischer, et al.. (1998). The Ets-1 Transcription Factor Is Required for the Development of Natural Killer Cells in Mice. Immunity. 9(4). 555–563. 295 indexed citations
19.
Kuo, Chay T., Margaret Veselits, Kevin Barton, et al.. (1997). The LKLF transcription factor is required for normal tunica media formation and blood vessel stabilization during murine embryogenesis. Genes & Development. 11(22). 2996–3006. 299 indexed citations
20.
Ting, Chao-Nan, et al.. (1996). Transcription factor GATA-3 is required for development of the T-cell lineage. Nature. 384(6608). 474–478. 516 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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