Mary Farrington

1.6k total citations · 1 hit paper
14 papers, 1.2k citations indexed

About

Mary Farrington is a scholar working on Immunology and Allergy, Immunology and Physiology. According to data from OpenAlex, Mary Farrington has authored 14 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Immunology and Allergy, 7 papers in Immunology and 3 papers in Physiology. Recurrent topics in Mary Farrington's work include Food Allergy and Anaphylaxis Research (7 papers), Allergic Rhinitis and Sensitization (4 papers) and Immune Cell Function and Interaction (4 papers). Mary Farrington is often cited by papers focused on Food Allergy and Anaphylaxis Research (7 papers), Allergic Rhinitis and Sensitization (4 papers) and Immune Cell Function and Interaction (4 papers). Mary Farrington collaborates with scholars based in United States, France and Spain. Mary Farrington's co-authors include Shigeaki Nonoyama, Alejandro Aruffo, Hans D. Ochs, Robert L. Roberts, Ronald E. Stenkamp, Jürgen Bajorath, R J Noelle, Athena Milatovich, Uta Francke and J A Ledbetter and has published in prestigious journals such as Cell, Journal of Allergy and Clinical Immunology and Science Translational Medicine.

In The Last Decade

Mary Farrington

13 papers receiving 1.1k citations

Hit Papers

The CD40 ligand, gp39, is defective in activated T cells ... 1993 2026 2004 2015 1993 200 400 600
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.

  1. The CD40 ligand, gp39, is defective in activated T cells from patients with X-linked hyper-IgM syndrome
    1993 663 citations Alejandro Aruffo, Mary Farrington et al. Cell profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mary Farrington United States 10 817 316 221 134 92 14 1.2k
Ayako Inamine Japan 10 685 0.8× 147 0.5× 143 0.6× 220 1.6× 51 0.6× 13 1.1k
Heidi Heath United States 7 975 1.2× 133 0.4× 252 1.1× 157 1.2× 66 0.7× 11 1.4k
Petra Schmidt Germany 9 970 1.2× 192 0.6× 83 0.4× 221 1.6× 31 0.3× 14 1.3k
K N Clifford Canada 10 1.6k 1.9× 221 0.7× 91 0.4× 283 2.1× 74 0.8× 10 1.9k
Christiane Guret United States 12 663 0.8× 108 0.3× 82 0.4× 234 1.7× 28 0.3× 15 1.0k
Eugene C. Butcher United States 7 1.0k 1.3× 432 1.4× 40 0.2× 187 1.4× 61 0.7× 9 1.4k
Reinhard Kulke Germany 14 327 0.4× 97 0.3× 154 0.7× 147 1.1× 42 0.5× 14 707
D F Hankins United Kingdom 6 1.2k 1.4× 117 0.4× 50 0.2× 185 1.4× 59 0.6× 8 1.4k
Mridu Acharya United States 12 464 0.6× 150 0.5× 83 0.4× 142 1.1× 27 0.3× 24 712
Kristine Murphy United States 9 1.6k 2.0× 155 0.5× 162 0.7× 155 1.2× 41 0.4× 9 1.9k

Countries citing papers authored by Mary Farrington

Since Specialization
Citations

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

Fields of papers citing papers by Mary Farrington

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mary Farrington

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

All Works

14 of 14 papers shown
1.
Gómez, R., Jue Hou, Vivian H. Gersuk, et al.. (2025). Ara h 2 105‐124 ‐Specific TH2A Cells Drive Peanut Allergy in DRB1*15:01 Individuals: A Detailed Epitope Analysis. Clinical & Experimental Allergy. 55(9). 820–833.
2.
Bajzik, Véronique, Hannah A. DeBerg, Nahir Garabatos, et al.. (2022). Oral desensitization therapy for peanut allergy induces dynamic changes in peanut‐specific immune responses. Allergy. 77(8). 2534–2548. 30 indexed citations
3.
Garabatos, Nahir, Véronique Bajzik, Mary Farrington, et al.. (2021). Optimal human pathogenic TH2 cell effector function requires local epithelial cytokine signaling. Journal of Allergy and Clinical Immunology. 148(3). 867–875.e4. 27 indexed citations
4.
Wambre, Erik, Mary Farrington, Véronique Bajzik, et al.. (2021). Clinical and immunological evaluation of cat‐allergic asthmatics living with or without a cat. Clinical & Experimental Allergy. 51(12). 1624–1633. 7 indexed citations
5.
Cerosaletti, Karen, Janice Chen, Nahir Garabatos, et al.. (2020). Development of peanut allergy is associated with coding SNPs in the C-terminus of the IL-33 receptor that impact signaling and TH2 effector function. Journal of Allergy and Clinical Immunology. 145(2). AB88–AB88. 1 indexed citations
6.
Wambre, Erik, Véronique Bajzik, Jonathan H. DeLong, et al.. (2017). A phenotypically and functionally distinct human T H 2 cell subpopulation is associated with allergic disorders. Science Translational Medicine. 9(401). 275 indexed citations
7.
McGinty, John W., Amédée Renand, David Jeong, et al.. (2016). Ana o 1 and Ana o 2 cashew allergens share cross‐reactive CD4+ T cell epitopes with other tree nuts. Clinical & Experimental Allergy. 46(6). 871–883. 26 indexed citations
8.
Jeong, David, et al.. (2016). Role of T Cell Sub-Populations in Food Allergy. Journal of Allergy and Clinical Immunology. 137(2). AB195–AB195. 1 indexed citations
9.
Jeong, David, Mariona Pascal, Joan Bartra, et al.. (2015). Jug r 2–reactive CD4+ T cells have a dominant immune role in walnut allergy. Journal of Allergy and Clinical Immunology. 136(4). 983–992.e7. 27 indexed citations
10.
Nonoyama, Shigeaki, Mary Farrington, & Hans D. Ochs. (1994). Effect of IL-2 on Immunoglobulin Production by Anti-CD40-Activated Human B Cells: Synergistic Effect with IL-10 and Antagonistic Effect with IL-4. Clinical Immunology and Immunopathology. 72(3). 373–379. 25 indexed citations
11.
Aruffo, Alejandro, Mary Farrington, Diane Hollenbaugh, et al.. (1993). The CD40 ligand, gp39, is defective in activated T cells from patients with X-linked hyper-IgM syndrome. Cell. 72(2). 291–300. 663 indexed citations breakdown →
12.
Ochs, H D, Shigeaki Nonoyama, Qili Zhu, Mary Farrington, & Ralph Wedgwood. (1993). Regulation of Antibody Responses: The Role of Complement and Adhesion Molecules. Clinical Immunology and Immunopathology. 67(3). S33–S40. 49 indexed citations
13.
Farrington, Mary, et al.. (1993). Eosinophilic fasciitis in children frequently progresses to scleroderma-like cutaneous fibrosis.. PubMed. 20(1). 128–32. 27 indexed citations
14.
Ochs, Hans D., Shigeaki Nonoyama, Mary Farrington, Susanna Fischer, & Alejandro Aruffo. (1993). The role of adhesion molecules in the regulation of antibody responses.. PubMed. 30(4 Suppl 4). 72–9; discussion 80. 11 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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