Douglas Marvel

1.7k total citations · 1 hit paper
6 papers, 836 citations indexed

About

Douglas Marvel is a scholar working on Immunology, Molecular Biology and Surgery. According to data from OpenAlex, Douglas Marvel has authored 6 papers receiving a total of 836 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Immunology, 3 papers in Molecular Biology and 1 paper in Surgery. Recurrent topics in Douglas Marvel's work include Immunotherapy and Immune Responses (3 papers), vaccines and immunoinformatics approaches (2 papers) and T-cell and B-cell Immunology (2 papers). Douglas Marvel is often cited by papers focused on Immunotherapy and Immune Responses (3 papers), vaccines and immunoinformatics approaches (2 papers) and T-cell and B-cell Immunology (2 papers). Douglas Marvel collaborates with scholars based in United States. Douglas Marvel's co-authors include Dmitry I. Gabrilovich, Olivera J. Finn, Adam M. Farkas, Duane A. Sewell, Benjamin S. Bleier, Erica R. Thaler, Alexander G. Chiu and Kathleen T. Montone and has published in prestigious journals such as Journal of Clinical Investigation, The Journal of Immunology and Cancer Research.

In The Last Decade

Douglas Marvel

6 papers receiving 829 citations

Hit Papers

align trajectories

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.

Myeloid-derived suppressor cells in the tumor microenvironment: expect the unexpected

2015 822 citations Douglas Marvel, Dmitry I. Gabrilovich Journal of Clinical Investigation profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Douglas Marvel United States	3	660	412	198	65	60	6	836
Anna-Maria Jegg Switzerland	4	538 0.8×	397 1.0×	205 1.0×	76 1.2×	60 1.0×	4	836
Elio Schouppe Belgium	10	764 1.2×	413 1.0×	274 1.4×	141 2.2×	85 1.4×	12	1.0k
Valeria Mollica Poeta Italy	6	391 0.6×	395 1.0×	243 1.2×	84 1.3×	47 0.8×	7	755
Jiemiao Hu United States	15	379 0.6×	327 0.8×	172 0.9×	46 0.7×	62 1.0×	32	657
Benxia Zhang China	6	400 0.6×	364 0.9×	184 0.9×	66 1.0×	86 1.4×	10	696
Qizhi Ma China	8	421 0.6×	395 1.0×	224 1.1×	72 1.1×	96 1.6×	17	753
Gaël Sugano United Kingdom	7	505 0.8×	368 0.9×	305 1.5×	131 2.0×	50 0.8×	7	784
James L. Sung United States	5	392 0.6×	332 0.8×	219 1.1×	71 1.1×	21 0.3×	5	674
Rana Mhaidly France	11	314 0.5×	375 0.9×	285 1.4×	135 2.1×	54 0.9×	19	745
Yuedi Wang China	8	300 0.5×	280 0.7×	196 1.0×	83 1.3×	52 0.9×	10	573

Countries citing papers authored by Douglas Marvel

Since Specialization

Citations

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

Fields of papers citing papers by Douglas Marvel

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Douglas Marvel

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

All Works

Sort: Min cites: Since: Top N: Style:

6 of 6 papers shown

1.

Marvel, Douglas & Dmitry I. Gabrilovich. (2015). Abstract 4051: Protection from CTL mediated killing by iNOS expression in a mouse model of melanoma. Cancer Research. 75(15_Supplement). 4051–4051. 1 indexed citations

2.

Marvel, Douglas & Dmitry I. Gabrilovich. (2015). Myeloid-derived suppressor cells in the tumor microenvironment: expect the unexpected. Journal of Clinical Investigation. 125(9). 3356–3364. 822 indexed citations breakdown →

3.

Marvel, Douglas & Olivera J. Finn. (2014). Global Inhibition of DC Priming Capacity in the Spleen of Self-Antigen Vaccinated Mice Requires IL-10. Frontiers in Immunology. 5. 59–59. 5 indexed citations

4.

Farkas, Adam M., Douglas Marvel, & Olivera J. Finn. (2013). Antigen Choice Determines Vaccine-Induced Generation of Immunogenic versus Tolerogenic Dendritic Cells That Are Marked by Differential Expression of Pancreatic Enzymes. The Journal of Immunology. 190(7). 3319–3327. 5 indexed citations

5.

Marvel, Douglas & Olivera J. Finn. (2013). Antigen-specific Treg and IL-10 influence DC immunogenicity hours following vaccination with a self versus a foreign antigen (P4232). The Journal of Immunology. 190(Supplement_1). 47.4–47.4. 1 indexed citations

6.

Bleier, Benjamin S., Erica R. Thaler, Duane A. Sewell, et al.. (2008). Diffuse Intranasal Papillomatosis and Its Association With Human Papillomavirus. Archives of Otolaryngology - Head and Neck Surgery. 134(7). 778–778. 2 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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