Yaron Tomer

8.6k total citations
119 papers, 6.1k citations indexed

About

Yaron Tomer is a scholar working on Genetics, Endocrinology, Diabetes and Metabolism and Immunology. According to data from OpenAlex, Yaron Tomer has authored 119 papers receiving a total of 6.1k indexed citations (citations by other indexed papers that have themselves been cited), including 64 papers in Genetics, 54 papers in Endocrinology, Diabetes and Metabolism and 40 papers in Immunology. Recurrent topics in Yaron Tomer's work include Diabetes and associated disorders (57 papers), Thyroid Disorders and Treatments (49 papers) and T-cell and B-cell Immunology (20 papers). Yaron Tomer is often cited by papers focused on Diabetes and associated disorders (57 papers), Thyroid Disorders and Treatments (49 papers) and T-cell and B-cell Immunology (20 papers). Yaron Tomer collaborates with scholars based in United States, Israel and Italy. Yaron Tomer's co-authors include Terry F. Davies, Amanda K. Huber, Eric M. Jacobson, Erlinda Concepcion, Giuseppe Barbesino, David A. Greenberg, Yehuda Shoenfeld, Yoshiyuki Ban, Jason T. Blackard and Francesca Menconi and has published in prestigious journals such as Journal of Biological Chemistry, PLoS ONE and Hepatology.

In The Last Decade

Yaron Tomer

119 papers receiving 5.9k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yaron Tomer United States 46 2.7k 2.7k 2.0k 776 766 119 6.1k
Yoshinori Iwatani Japan 35 1.4k 0.5× 827 0.3× 1.4k 0.7× 702 0.9× 524 0.7× 177 4.2k
Lars P. Ryder Denmark 43 928 0.3× 2.1k 0.8× 3.8k 1.9× 891 1.1× 750 1.0× 211 7.7k
Corrado Betterle Italy 44 3.7k 1.4× 2.0k 0.7× 832 0.4× 496 0.6× 556 0.7× 191 6.0k
Patrizio Caturegli United States 41 3.3k 1.2× 754 0.3× 1.2k 0.6× 946 1.2× 531 0.7× 149 6.8k
P. Platz Denmark 33 809 0.3× 1.7k 0.6× 1.9k 1.0× 370 0.5× 380 0.5× 114 4.3k
Mónica Marazuela Spain 38 1.8k 0.7× 548 0.2× 1.1k 0.5× 1.1k 1.4× 180 0.2× 202 5.2k
Dag E. Undlien Norway 37 1.7k 0.6× 2.8k 1.0× 1.3k 0.6× 1.3k 1.7× 239 0.3× 97 5.2k
Gian Franco Bottazzo United Kingdom 35 2.7k 1.0× 3.4k 1.3× 1.6k 0.8× 985 1.3× 244 0.3× 94 6.3k
Janelle A. Noble United States 34 1.7k 0.6× 3.1k 1.1× 1.9k 0.9× 859 1.1× 302 0.4× 98 5.0k
W J Irvine United Kingdom 47 2.9k 1.1× 2.1k 0.8× 1.2k 0.6× 638 0.8× 400 0.5× 177 5.9k

Countries citing papers authored by Yaron Tomer

Since Specialization
Citations

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

Fields of papers citing papers by Yaron Tomer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yaron Tomer

This figure shows the co-authorship network connecting the top 25 collaborators of Yaron Tomer. A scholar is included among the top collaborators of Yaron Tomer 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 Yaron Tomer. Yaron Tomer 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.
2.
Tomer, Yaron, et al.. (2021). Electronic Health Record Usage Patterns: Assessing Telemedicine's Impact on the Provider Experience During the COVID-19 Pandemic. Telemedicine Journal and e-Health. 27(8). 934–938. 14 indexed citations
3.
Ye, Yi, et al.. (2021). Genetic and environmental factors regulate the type 1 diabetes gene CTSH via differential DNA methylation. Journal of Biological Chemistry. 296. 100774–100774. 18 indexed citations
4.
Lombardi, Angela, Erlinda Concepcion, Hanane Arib, et al.. (2020). Retro-inverso D-peptides as a novel targeted immunotherapy for Type 1 diabetes. Journal of Autoimmunity. 115. 102543–102543. 13 indexed citations
5.
6.
Karaköse, Esra, et al.. (2019). Epigenetic modulation of β cells by interferon-α via PNPT1/mir-26a/TET2 triggers autoimmune diabetes. JCI Insight. 4(5). 32 indexed citations
7.
Lombardi, Angela, et al.. (2018). Interferon alpha: The key trigger of type 1 diabetes. Journal of Autoimmunity. 94. 7–15. 61 indexed citations
8.
Lambertini, Luca, Alan B. Copperman, Sara Salehi Hammerstad, et al.. (2017). Intrauterine Reprogramming of the Polycystic Ovary Syndrome: Evidence from a Pilot Study of Cord Blood Global Methylation Analysis. Frontiers in Endocrinology. 8. 352–352. 41 indexed citations
9.
Li, Cheuk Wun, Francesca Menconi, Roman Osman, et al.. (2015). Identifying a Small Molecule Blocking Antigen Presentation in Autoimmune Thyroiditis. Journal of Biological Chemistry. 291(8). 4079–4090. 24 indexed citations
10.
Stefan, Mihaela, Eric M. Jacobson, Amanda K. Huber, et al.. (2011). Novel Variant of Thyroglobulin Promoter Triggers Thyroid Autoimmunity through an Epigenetic Interferon α-modulated Mechanism. Journal of Biological Chemistry. 286(36). 31168–31179. 54 indexed citations
11.
Tomer, Yaron. (2010). Genetic Susceptibility to Autoimmune Thyroid Disease: Past, Present, and Future. Thyroid. 20(7). 715–725. 159 indexed citations
12.
Huber, Amanda K., Eric M. Jacobson, Krystian Jażdżewski, Erlinda Concepcion, & Yaron Tomer. (2007). Interleukin (IL)-23 Receptor Is a Major Susceptibility Gene for Graves’ Ophthalmopathy: The IL-23/T-helper 17 Axis Extends to Thyroid Autoimmunity. The Journal of Clinical Endocrinology & Metabolism. 93(3). 1077–1081. 115 indexed citations
13.
Yin, Xiao‐Ming, Rauf Latif, Yaron Tomer, & Terry F. Davies. (2007). Thyroid Epigenetics. Annals of the New York Academy of Sciences. 1110(1). 193–200. 68 indexed citations
14.
Kim, Grace, Yoshiyuki Ban, Pamela D. Unger, et al.. (2003). The Effects of Alpha Interferon on the Development of Autoimmune Thyroiditis in the NOD H2h4 Mouse. Journal of Immunology Research. 10(2-4). 161–165. 13 indexed citations
15.
Barbesino, Giuseppe, Yaron Tomer, Erlinda Concepcion, Terry F. Davies, & David A. Greenberg. (1998). Linkage Analysis of Candidate Genes in Autoimmune Thyroid Disease. II. Selected Gender-Related Genes and the X-Chromosome1. The Journal of Clinical Endocrinology & Metabolism. 83(9). 3290–3295. 42 indexed citations
16.
Tomer, Yaron, Ofer Lider, Boris Gilburd, et al.. (1997). Anti-neutrophil Cytoplasmic Antibody-Enriched IgG Induces Adhesion of Human T Lymphocytes to Extracellular Matrix Proteins. Clinical Immunology and Immunopathology. 83(3). 245–253. 5 indexed citations
17.
Adler, Yehuda, Gisele Zandman‐Goddard, M Ravid, et al.. (1994). Usefulness of colchicine in preventing recurrences of pericarditis. The American Journal of Cardiology. 73(12). 916–917. 60 indexed citations
18.
Tomer, Yaron, Dan Buskila, & Yehuda Shoenfeld. (1993). Pathogenic Significance and Diagnostic Value of Lupus Autoantibodies. International Archives of Allergy and Immunology. 100(4). 293–306. 19 indexed citations
19.
Tomer, Yaron, et al.. (1991). Effects of aging on the induction of experimental systemic lupus erythematosus (SLE) in mice. Mechanisms of Ageing and Development. 58(2-3). 233–244. 17 indexed citations
20.
Tomer, Yaron & Yehuda Shoenfeld. (1989). The significance of T suppressor cells in the development of autoimmunity. Journal of Autoimmunity. 2(6). 739–758. 24 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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