Dianna Wu

2.2k total citations
21 papers, 1.1k citations indexed

About

Dianna Wu is a scholar working on Immunology, Oncology and Molecular Biology. According to data from OpenAlex, Dianna Wu has authored 21 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Immunology, 9 papers in Oncology and 6 papers in Molecular Biology. Recurrent topics in Dianna Wu's work include Cancer Immunotherapy and Biomarkers (8 papers), Immune Cell Function and Interaction (6 papers) and Immunotherapy and Immune Responses (5 papers). Dianna Wu is often cited by papers focused on Cancer Immunotherapy and Biomarkers (8 papers), Immune Cell Function and Interaction (6 papers) and Immunotherapy and Immune Responses (5 papers). Dianna Wu collaborates with scholars based in United States, Denmark and Australia. Dianna Wu's co-authors include Paul B. Chapman, Irving Goldschneider, Mitchell Kronenberg, Stéphane Sidobre, Neil H. Segal, Kenneth Emancipator, Marisa Dolled‐Filhart, Shane L. Rea, Anatoliy I. Yashin and Tyler D. Johnson and has published in prestigious journals such as The Journal of Experimental Medicine, Journal of Clinical Oncology and The Journal of Immunology.

In The Last Decade

Dianna Wu

21 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Dianna Wu United States 15 615 470 209 194 67 21 1.1k
Yuko Kozono Japan 14 820 1.3× 205 0.4× 279 1.3× 40 0.2× 34 0.5× 18 1.2k
Jonathan Stevens United States 8 650 1.1× 455 1.0× 536 2.6× 50 0.3× 24 0.4× 13 1.0k
Michelle L. Badura United States 9 432 0.7× 577 1.2× 380 1.8× 128 0.7× 184 2.7× 12 1.1k
David Woods United States 17 587 1.0× 877 1.9× 890 4.3× 127 0.7× 96 1.4× 42 1.6k
Rena Morita Japan 15 303 0.5× 577 1.2× 429 2.1× 63 0.3× 71 1.1× 36 943
Kelly M. Ramsbottom Australia 16 654 1.1× 462 1.0× 414 2.0× 46 0.2× 41 0.6× 22 1.0k
Philip Komarnitsky United States 19 129 0.2× 462 1.0× 1.3k 6.4× 138 0.7× 40 0.6× 48 1.7k
Lisa J. McReynolds United States 19 520 0.8× 293 0.6× 643 3.1× 70 0.4× 40 0.6× 51 1.4k
Amy M. McCord United States 7 183 0.3× 342 0.7× 222 1.1× 36 0.2× 28 0.4× 11 635
Christine Alewine United States 18 395 0.6× 517 1.1× 295 1.4× 299 1.5× 58 0.9× 51 1.1k

Countries citing papers authored by Dianna Wu

Since Specialization
Citations

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

Fields of papers citing papers by Dianna Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dianna Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Dianna Wu. A scholar is included among the top collaborators of Dianna Wu 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 Dianna Wu. Dianna Wu 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.
Shi, Ying, Puyuan Xing, Yue Guan, et al.. (2018). P1.13-18 Exploring the Resistance Mechanism of Osimertinib and Monitoring the Treatment Response Using Plasma ctDNA in Chinese NSCLC Patients. Journal of Thoracic Oncology. 13(10). S589–S589. 9 indexed citations
2.
Sun, Jong‐Mu, Wei Zhou, Yoon‐La Choi, et al.. (2016). Prognostic Significance of PD-L1 in Patients with Non–Small Cell Lung Cancer: A Large Cohort Study of Surgically Resected Cases. Journal of Thoracic Oncology. 11(7). 1003–1011. 129 indexed citations
3.
Dolled‐Filhart, Marisa, Darren Locke, Jennifer H. Yearley, et al.. (2016). Development of a Prototype Immunohistochemistry Assay to Measure Programmed Death Ligand-1 Expression in Tumor Tissue. Archives of Pathology & Laboratory Medicine. 140(11). 1259–1266. 56 indexed citations
4.
Zhou, Wei, Marisa Dolled‐Filhart, Zhen Wang, et al.. (2016). PD-L1 Expression and Survival among Patients with Advanced Non–Small Cell Lung Cancer Treated with Chemotherapy. Translational Oncology. 9(1). 64–69. 78 indexed citations
5.
Steiniche, Torben, Zhen Wang, Patricia Switten Nielsen, et al.. (2015). Abstract 4303: Programmed death ligand 1 (PD-L1) expression in paired melanoma tumor samples. Cancer Research. 75(15_Supplement). 4303–4303. 1 indexed citations
6.
Sørensen, Steen, Wei Zhou, Marisa Dolled‐Filhart, et al.. (2014). Pd-L1 Expression and Survival Among Advanced Non–Small Cell Lung Cancer (Nsclc) Patients Treated with Chemotherapy. Annals of Oncology. 25. iv467–iv467. 4 indexed citations
7.
Garcia‐Manero, Guillermo, Giovanni Martinelli, Joshua F. Zeidner, et al.. (2014). A multicohort trial of the safety and efficacy of the PD-1 inhibitor MK-3475 in patients with hematologic malignancies.. Journal of Clinical Oncology. 32(15_suppl). TPS3116–TPS3116. 3 indexed citations
8.
Townsend, Robert, Yun Shen, Dong Geng, et al.. (2013). Pharmacokinetics, Pharmacodynamics, and Immunogenicity of Belatacept in Adult Kidney Transplant Recipients. Clinical Drug Investigation. 34(2). 117–126. 29 indexed citations
9.
Weber, Jeffrey S., Omid Hamid, Scott D. Chasalow, et al.. (2011). Ipilimumab Increases Activated T Cells and Enhances Humoral Immunity in Patients With Advanced Melanoma. Journal of Immunotherapy. 35(1). 89–97. 98 indexed citations
10.
Xu, Yuanxin, et al.. (2010). Recommendations for the validation of flow cytometric testing during drug development: II assays. Journal of Immunological Methods. 363(2). 120–134. 87 indexed citations
11.
Wu, Dianna, et al.. (2010). Development and Validation of Flow Cytometry Methods for Pharmacodynamic Clinical Biomarkers. Bioanalysis. 2(9). 1617–1626. 19 indexed citations
12.
Berman, David M., Susan Parker, Scott D. Chasalow, et al.. (2008). Potential immune biomarkers of gastrointestinal toxicities and efficacy in patients with advanced melanoma treated with ipilimumab with or without prophylactic budesonide. Journal of Clinical Oncology. 26(15_suppl). 3022–3022. 9 indexed citations
13.
14.
Wu, Dianna, Shane L. Rea, Anatoliy I. Yashin, & Tyler D. Johnson. (2006). Visualizing hidden heterogeneity in isogenic populations of C. elegans. Experimental Gerontology. 41(3). 261–270. 70 indexed citations
15.
Chapman, Paul B., Dianna Wu, Govind Ragupathi, et al.. (2004). Sequential Immunization of Melanoma Patients with GD3 Ganglioside Vaccine and Anti-Idiotypic Monoclonal Antibody That Mimics GD3 Ganglioside. Clinical Cancer Research. 10(14). 4717–4723. 49 indexed citations
16.
Wu, Dianna, Neil H. Segal, Stéphane Sidobre, Mitchell Kronenberg, & Paul B. Chapman. (2003). Cross-presentation of Disialoganglioside GD3 to Natural Killer T Cells. The Journal of Experimental Medicine. 198(1). 173–181. 228 indexed citations
17.
Wu, Dianna & Irving Goldschneider. (2001). Tolerance to Cyclosporin A-Induced Autologous Graft-Versus-Host Disease Is Mediated by a CD4+CD25+ Subset of Recent Thymic Emigrants. The Journal of Immunology. 166(12). 7158–7164. 19 indexed citations
18.
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20.
Zadeh, Homayoun H., et al.. (1996). Abnormalities in the Export and Fate of Recent Thymic Emigrants in Diabetes-Prone BB/W Rats1. Autoimmunity. 24(1). 35–46. 47 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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