Tao Dao

4.0k total citations
77 papers, 3.1k citations indexed

About

Tao Dao is a scholar working on Oncology, Molecular Biology and Immunology. According to data from OpenAlex, Tao Dao has authored 77 papers receiving a total of 3.1k indexed citations (citations by other indexed papers that have themselves been cited), including 44 papers in Oncology, 41 papers in Molecular Biology and 39 papers in Immunology. Recurrent topics in Tao Dao's work include CAR-T cell therapy research (35 papers), Immunotherapy and Immune Responses (30 papers) and Renal and related cancers (21 papers). Tao Dao is often cited by papers focused on CAR-T cell therapy research (35 papers), Immunotherapy and Immune Responses (30 papers) and Renal and related cancers (21 papers). Tao Dao collaborates with scholars based in United States, Japan and Czechia. Tao Dao's co-authors include David A. Scheinberg, Ian Nicholas Crispe, Wajahat Z. Mehal, Tatyana Korontsvit, Daniela Metz, Katja Klugewitz, Ravi Hingorani, Haruki Okamura, Kunihiro Ohashi and Tohru Kayano and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and Journal of Clinical Investigation.

In The Last Decade

Tao Dao

77 papers receiving 3.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tao Dao United States 28 1.8k 1.3k 1.2k 334 249 77 3.1k
Yajun Guo China 34 1.2k 0.7× 1.1k 0.8× 1.2k 1.0× 553 1.7× 177 0.7× 95 3.1k
Lianjun Shen China 17 1.6k 0.9× 750 0.6× 1.4k 1.2× 120 0.4× 208 0.8× 49 2.8k
Hinrich P. Hansen Germany 29 1.5k 0.8× 949 0.7× 1.3k 1.1× 353 1.1× 167 0.7× 72 3.2k
Georg H. Fey Germany 35 1.3k 0.7× 1.1k 0.9× 1.3k 1.1× 567 1.7× 93 0.4× 71 3.1k
Eva Lion Belgium 29 1.9k 1.0× 1.3k 1.0× 731 0.6× 198 0.6× 215 0.9× 64 2.6k
Jon Arnason United States 27 1.4k 0.7× 1.6k 1.2× 615 0.5× 161 0.5× 220 0.9× 120 3.2k
Erik Hooijberg Netherlands 34 2.0k 1.1× 1.4k 1.1× 1.1k 0.9× 217 0.6× 97 0.4× 95 3.2k
Shivani Srivastava United States 25 1.8k 1.0× 1.9k 1.4× 740 0.6× 104 0.3× 484 1.9× 58 3.3k
Pierre L. Triozzi United States 33 1.3k 0.7× 1.8k 1.4× 1.4k 1.1× 317 0.9× 156 0.6× 152 3.9k
Amy Hobeika United States 34 2.5k 1.4× 1.7k 1.3× 2.0k 1.6× 296 0.9× 200 0.8× 90 4.2k

Countries citing papers authored by Tao Dao

Since Specialization
Citations

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

Fields of papers citing papers by Tao Dao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tao Dao

This figure shows the co-authorship network connecting the top 25 collaborators of Tao Dao. A scholar is included among the top collaborators of Tao Dao 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 Tao Dao. Tao Dao 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.
Liu, Bingxu, Tao Dao, Xinyu Xiang, et al.. (2025). Design of high-specificity binders for peptide–MHC-I complexes. Science. 389(6758). 386–391. 5 indexed citations
2.
Dao, Tao, Guangyan Xiong, Tatyana Korontsvit, et al.. (2023). A dual-receptor T-cell platform with Ab-TCR and costimulatory receptor achieves specificity and potency against AML. Blood. 143(6). 507–521. 18 indexed citations
3.
Malviya, Manish, et al.. (2023). Challenges and solutions for therapeuticTCR‐based agents. Immunological Reviews. 320(1). 58–82. 17 indexed citations
4.
Klatt, Martin G., et al.. (2023). The landscape of MHC-presented phosphopeptides yields actionable shared tumor antigens for cancer immunotherapy across multiple HLA alleles. Journal for ImmunoTherapy of Cancer. 11(9). e006889–e006889. 4 indexed citations
5.
Gejman, Ron S., Martin G. Klatt, Aaron Y. Chang, et al.. (2020). Identification of the Targets of T-cell Receptor Therapeutic Agents and Cells by Use of a High-Throughput Genetic Platform. Cancer Immunology Research. 8(5). 672–684. 26 indexed citations
6.
Klatt, Martin G., et al.. (2020). Solving an MHC allele–specific bias in the reported immunopeptidome. JCI Insight. 5(19). 15 indexed citations
7.
Dao, Tao, Martin G. Klatt, Tatyana Korontsvit, et al.. (2020). Impact of tumor heterogeneity and microenvironment in identifying neoantigens in a patient with ovarian cancer. Cancer Immunology Immunotherapy. 70(5). 1189–1202. 8 indexed citations
8.
Oh, Claire Y., Martin G. Klatt, Christopher M. Bourne, et al.. (2019). ALK and RET Inhibitors Promote HLA Class I Antigen Presentation and Unmask New Antigens within the Tumor Immunopeptidome. Cancer Immunology Research. 7(12). 1984–1997. 31 indexed citations
9.
Zauderer, Marjorie G., Anne S. Tsao, Tao Dao, et al.. (2017). A Randomized Phase II Trial of Adjuvant Galinpepimut-S, WT-1 Analogue Peptide Vaccine, After Multimodality Therapy for Patients with Malignant Pleural Mesothelioma. Clinical Cancer Research. 23(24). 7483–7489. 46 indexed citations
10.
Gejman, Ron S., Tao Dao, & David A. Scheinberg. (2017). Identification of Cross-Reactive Targets of TCR-Based Therapeutic Agents within the Human Proteome. Blood. 130. 3188–3188. 1 indexed citations
11.
Chang, Aaron Y., Tao Dao, Ron S. Gejman, et al.. (2017). A therapeutic T cell receptor mimic antibody targets tumor-associated PRAME peptide/HLA-I antigens. Journal of Clinical Investigation. 127(7). 2705–2718. 68 indexed citations
12.
Rafiq, Sarwish, Terence J. Purdon, Anthony F. Daniyan, et al.. (2016). Optimized T-cell receptor-mimic chimeric antigen receptor T cells directed toward the intracellular Wilms Tumor 1 antigen. Leukemia. 31(8). 1788–1797. 128 indexed citations
13.
Dao, Tao, Dmitry Pankov, Andrew Scott, et al.. (2015). Therapeutic bispecific T-cell engager antibody targeting the intracellular oncoprotein WT1. Nature Biotechnology. 33(10). 1079–1086. 126 indexed citations
14.
Veomett, Nicholas, Tao Dao, Hong Liu, et al.. (2014). Therapeutic Efficacy of an Fc-Enhanced TCR-like Antibody to the Intracellular WT1 Oncoprotein. Clinical Cancer Research. 20(15). 4036–4046. 41 indexed citations
15.
Dao, Tao, et al.. (2009). Identification of a Human Cyclin D1-Derived Peptide that Induces Human Cytotoxic CD4 T Cells. PLoS ONE. 4(8). e6730–e6730. 14 indexed citations
16.
17.
Dao, Tao, J. Magarian Blander, & Derek B. Sant’Angelo. (2003). Recognition of a Specific Self-Peptide: Self-MHC Class II Complex Is Critical for Positive Selection of Thymocytes Expressing the D10 TCR. The Journal of Immunology. 170(1). 48–54. 12 indexed citations
18.
Dao, Tao, Mark A. Exley, Wajahat Z. Mehal, et al.. (2001). Involvement of CD1 in Peripheral Deletion of T Lymphocytes Is Independent of NK T Cells. The Journal of Immunology. 166(5). 3090–3097. 15 indexed citations
19.
Crispe, Ian Nicholas, Tao Dao, Katja Klugewitz, Wajahat Z. Mehal, & Daniela Metz. (2000). The liver as a site of T-cell apoptosis: graveyard, or killing field?. Immunological Reviews. 174(1). 47–62. 254 indexed citations
20.
Holáň, Vladimı́r, et al.. (1994). Effects of Direct Current on T Cell Activity: Modulation of Interleukin-2 Production. Immunobiology. 190(4-5). 368–375. 5 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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