Tammy Huang

6.7k total citations · 2 hit papers
16 papers, 2.7k citations indexed

About

Tammy Huang is a scholar working on Molecular Biology, Oncology and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Tammy Huang has authored 16 papers receiving a total of 2.7k indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 6 papers in Oncology and 6 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Tammy Huang's work include Monoclonal and Polyclonal Antibodies Research (6 papers), CAR-T cell therapy research (4 papers) and IL-33, ST2, and ILC Pathways (3 papers). Tammy Huang is often cited by papers focused on Monoclonal and Polyclonal Antibodies Research (6 papers), CAR-T cell therapy research (4 papers) and IL-33, ST2, and ILC Pathways (3 papers). Tammy Huang collaborates with scholars based in United States and Sweden. Tammy Huang's co-authors include George D. Yancopoulos, Nikolaos G. Papadopoulos, John S. Rudge, Michelle Russell, Jocelyn Holash, James P. Fandl, Donna Hylton, Czeslaw Radziejewski, Stanley J. Wiegand and Ella Ioffe and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Blood and Cancer Research.

In The Last Decade

Tammy Huang

15 papers receiving 2.7k citations

Hit Papers

VEGF-Trap: A VEGF blocker... 2002 2026 2010 2018 2002 2019 400 800 1.2k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tammy Huang United States 10 1.2k 523 512 423 414 16 2.7k
Xavier Sastre France 33 822 0.7× 279 0.5× 2.0k 3.8× 714 1.7× 618 1.5× 77 4.2k
Bart Jonckx Belgium 15 961 0.8× 227 0.4× 426 0.8× 218 0.5× 324 0.8× 24 1.8k
Samia Mourah France 33 1.5k 1.2× 183 0.3× 1.3k 2.5× 74 0.2× 582 1.4× 162 3.1k
Lynne Krummen United States 26 2.2k 1.8× 478 0.9× 629 1.2× 185 0.4× 200 0.5× 34 3.3k
Ian Kasman United States 20 2.5k 2.0× 195 0.4× 1.5k 2.9× 105 0.2× 2.1k 5.1× 26 5.3k
J D Griffin United States 32 1.3k 1.0× 288 0.6× 897 1.8× 52 0.1× 1.5k 3.7× 47 3.7k
M. Nicotra Italy 26 1.1k 0.9× 495 0.9× 785 1.5× 41 0.1× 1.1k 2.6× 70 3.0k
Maresa Altomonte Italy 36 1.6k 1.3× 312 0.6× 2.1k 4.0× 97 0.2× 1.7k 4.1× 95 4.4k
William C. Manning United States 22 1.3k 1.1× 144 0.3× 606 1.2× 176 0.4× 451 1.1× 27 3.0k
Melissa R. Snyder United States 25 718 0.6× 1.0k 1.9× 338 0.7× 936 2.2× 749 1.8× 66 3.0k

Countries citing papers authored by Tammy Huang

Since Specialization
Citations

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

Fields of papers citing papers by Tammy Huang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tammy Huang

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

All Works

16 of 16 papers shown
1.
Jiang, Zaoli, Michael Rosconi, Bojie Zhang, et al.. (2024). A Tetravalent Bispecific Antibody Selectively Inhibits Diverse FGFR3 Oncogenic Variants. Cancer Research. 84(13). 2169–2180. 3 indexed citations
2.
Kamat, Vishal, Ashique Rafique, Jing Wang, et al.. (2021). High affinity human Fc specific monoclonal antibodies for capture kinetic analyses of antibody-antigen interactions. Analytical Biochemistry. 640. 114455–114455. 5 indexed citations
3.
Kamat, Vishal, Ashique Rafique, Tammy Huang, Olav Olsen, & William C. Olson. (2020). The impact of different human IgG capture molecules on the kinetics analysis of antibody-antigen interaction. Analytical Biochemistry. 593. 113580–113580. 25 indexed citations
4.
Allinne, Jeanne, George Scott, Wei Keat Lim, et al.. (2019). IL-33 blockade affects mediators of persistence and exacerbation in a model of chronic airway inflammation. Journal of Allergy and Clinical Immunology. 144(6). 1624–1637.e10. 87 indexed citations
5.
Floc’h, Audrey Le, Jeanne Allinne, Kirsten Nagashima, et al.. (2019). Dual blockade of IL‐4 and IL‐13 with dupilumab, an IL‐4Rα antibody, is required to broadly inhibit type 2 inflammation. Allergy. 75(5). 1188–1204. 330 indexed citations breakdown →
6.
Orengo, Jamie, Jeanne Allinne, Audrey Le Floc’h, et al.. (2018). Blocking IL-4Ra with dupilumab prevents lung inflammation in a mouse asthma model. PA977–PA977. 4 indexed citations
7.
Pascal, Kristen E., Christopher M. Coleman, Alejandro O. Mujica, et al.. (2015). Pre- and postexposure efficacy of fully human antibodies against Spike protein in a novel humanized mouse model of MERS-CoV infection. Proceedings of the National Academy of Sciences. 112(28). 8738–8743. 172 indexed citations
9.
Zhang, Li, Carla Castanaro, Jeanette Fairhurst, et al.. (2014). ERBB3/HER2 Signaling Promotes Resistance to EGFR Blockade in Head and Neck and Colorectal Cancer Models. Molecular Cancer Therapeutics. 13(5). 1345–1355. 42 indexed citations
10.
Murphy, Andrew, Lynn E. Macdonald, Sean Stevens, et al.. (2014). Mice with megabase humanization of their immunoglobulin genes generate antibodies as efficiently as normal mice. Proceedings of the National Academy of Sciences. 111(14). 5153–5158. 307 indexed citations
11.
Adler, Alexander P., Christopher Daly, Asma Parveen, et al.. (2014). Abstract 4492: Blockade of angiopoietin-2 or Tie2 is equally effective at inhibiting tumor growth and reducing tumor vessel density in most human tumor xenograft models. Cancer Research. 74(19_Supplement). 4492–4492. 2 indexed citations
12.
Rafique, Ashique, et al.. (2013). AB0037 Evaluation of the binding kinetics and functional bioassay activity of sarilumab and tocilizumab to the human il-6 receptor (il-6r) alpha. Annals of the Rheumatic Diseases. 72. A797–A797. 26 indexed citations
13.
Huang, Tammy, Chunyan Zhang, Иван Тодоров, et al.. (2004). Donor CD8+ T Cells Facilitate Induction of Chimerism and Tolerance without GVHD in Autoimmune NOD Mice Conditioned with Anti-CD3 mAb.. Blood. 104(11). 1204–1204. 1 indexed citations
14.
Huang, Tammy, Chunyan Zhang, Иван Тодоров, et al.. (2004). Donor CD8+ T cells facilitate induction of chimerism and tolerance without GVHD in autoimmune NOD mice conditioned with anti-CD3 mAb. Blood. 105(5). 2180–2188. 35 indexed citations
15.
Holash, Jocelyn, Nikolaos G. Papadopoulos, Susan D. Croll, et al.. (2002). VEGF-Trap: A VEGF blocker with potent antitumor effects. Proceedings of the National Academy of Sciences. 99(17). 11393–11398. 1325 indexed citations breakdown →
16.
Valenzuela, David M., Jennifer Griffiths, José Rojas, et al.. (1999). Angiopoietins 3 and 4: Diverging gene counterparts in mice and humans. Proceedings of the National Academy of Sciences. 96(5). 1904–1909. 376 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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