David M. Tobin

6.6k total citations · 1 hit paper
65 papers, 4.5k citations indexed

About

David M. Tobin is a scholar working on Immunology, Infectious Diseases and Epidemiology. According to data from OpenAlex, David M. Tobin has authored 65 papers receiving a total of 4.5k indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Immunology, 25 papers in Infectious Diseases and 22 papers in Epidemiology. Recurrent topics in David M. Tobin's work include Tuberculosis Research and Epidemiology (24 papers), Mycobacterium research and diagnosis (18 papers) and Zebrafish Biomedical Research Applications (12 papers). David M. Tobin is often cited by papers focused on Tuberculosis Research and Epidemiology (24 papers), Mycobacterium research and diagnosis (18 papers) and Zebrafish Biomedical Research Applications (12 papers). David M. Tobin collaborates with scholars based in United States, United Kingdom and Australia. David M. Tobin's co-authors include Lalita Ramakrishnan, Cornelia I. Bargmann, Mark R. Cronan, Rebecca W. Beerman, Stefan H. Oehlers, Wolfgang Liedtke, Jeffrey M. Friedman, Eric M. Walton, Erin L. Peckol and John Ray and has published in prestigious journals such as Nature, Cell and Proceedings of the National Academy of Sciences.

In The Last Decade

David M. Tobin

62 papers receiving 4.4k citations

Hit Papers

Host Genotype-Specific Therapies Can Optimize the Inflamm... 2012 2026 2016 2021 2012 100 200 300 400

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David M. Tobin United States 34 1.6k 1.4k 1.3k 1.1k 644 65 4.5k
Daniel Kalman United States 42 876 0.5× 960 0.7× 638 0.5× 2.5k 2.2× 491 0.8× 80 5.2k
Mika Rämet Finland 40 466 0.3× 3.7k 2.6× 789 0.6× 1.7k 1.5× 461 0.7× 121 6.7k
Lynda M. Stuart United States 35 571 0.4× 4.4k 3.1× 1.1k 0.8× 2.8k 2.5× 322 0.5× 54 7.5k
Eric J. Brown United States 43 1.0k 0.6× 1.9k 1.4× 1.4k 1.1× 4.7k 4.1× 950 1.5× 60 8.6k
Andrea J. Wolf United States 27 1.6k 1.0× 2.8k 2.0× 1.4k 1.1× 3.1k 2.7× 240 0.4× 30 6.8k
Huaijun Zhou United States 44 635 0.4× 1.1k 0.8× 769 0.6× 2.0k 1.8× 305 0.5× 161 5.6k
Kelly A. Ruhn United States 25 466 0.3× 1.1k 0.8× 417 0.3× 2.2k 1.9× 109 0.2× 32 4.7k
Robin J. Parks Canada 44 851 0.5× 937 0.7× 762 0.6× 5.5k 4.8× 292 0.5× 126 8.0k
Yasuhiro Yoshikawa Japan 35 799 0.5× 490 0.3× 796 0.6× 1.3k 1.1× 221 0.3× 244 4.6k
Cory Teuscher United States 46 502 0.3× 3.0k 2.1× 464 0.4× 1.9k 1.7× 166 0.3× 189 7.0k

Countries citing papers authored by David M. Tobin

Since Specialization
Citations

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

Fields of papers citing papers by David M. Tobin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David M. Tobin. 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 David M. Tobin. The network helps show where David M. Tobin may publish in the future.

Co-authorship network of co-authors of David M. Tobin

This figure shows the co-authorship network connecting the top 25 collaborators of David M. Tobin. A scholar is included among the top collaborators of David M. Tobin 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 David M. Tobin. David M. Tobin 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.
Allen, Judith E., et al.. (2025). Infectious Disease: Evolution, Mechanism and Global Health. Disease Models & Mechanisms. 18(9).
2.
Tobin, David M., et al.. (2025). Mycobacterium marinum as a model for understanding principles of mycobacterial pathogenesis. Journal of Bacteriology. 207(5). e0004725–e0004725. 2 indexed citations
3.
Yang, Yuwei, et al.. (2024). The antagonistic transcription factors, EspM and EspN, regulate the ESX-1 secretion system in M. marinum. mBio. 15(4). e0335723–e0335723. 6 indexed citations
5.
Whitworth, Laura, Antonio J. Pagán, Francisco J. Roca, et al.. (2021). Elevated cerebrospinal fluid cytokine levels in tuberculous meningitis predict survival in response to dexamethasone. Proceedings of the National Academy of Sciences. 118(10). 19 indexed citations
6.
Ordoñez, Alvaro A., Elizabeth W. Tucker, Carolyn J. Anderson, et al.. (2021). Visualizing the dynamics of tuberculosis pathology using molecular imaging. Journal of Clinical Investigation. 131(5). 17 indexed citations
7.
Wang, Yueyang, Alan Y. Hsu, Eric M. Walton, et al.. (2021). A robust and flexible CRISPR/Cas9-based system for neutrophil-specific gene inactivation in zebrafish. Journal of Cell Science. 134(8). 9 indexed citations
8.
Cronan, Mark R. & David M. Tobin. (2019). Endogenous Tagging at the cdh1 Locus for Live Visualization of E-Cadherin Dynamics. Zebrafish. 16(3). 324–325. 10 indexed citations
9.
Hortle, Elinor, Matt D. Johansen, Jordan A. Shavit, et al.. (2019). Thrombocyte Inhibition Restores Protective Immunity to Mycobacterial Infection in Zebrafish. The Journal of Infectious Diseases. 220(3). 524–534. 23 indexed citations
10.
McClean, Colleen M. & David M. Tobin. (2016). Macrophage form, function, and phenotype in mycobacterial infection: lessons from tuberculosis and other diseases. Pathogens and Disease. 74(7). ftw068–ftw068. 100 indexed citations
11.
Tobin, David M.. (2015). Host-Directed Therapies for Tuberculosis: Figure 1.. Cold Spring Harbor Perspectives in Medicine. 5(10). a021196–a021196. 88 indexed citations
12.
Whipps, Christopher M., et al.. (2014). Detection of Autofluorescent Mycobacterium Chelonae in Living Zebrafish. Zebrafish. 11(1). 76–82. 8 indexed citations
13.
Furuse, Yuki, Ryan Finethy, Héctor A. Saka, et al.. (2014). Search for MicroRNAs Expressed by Intracellular Bacterial Pathogens in Infected Mammalian Cells. PLoS ONE. 9(9). e106434–e106434. 56 indexed citations
14.
Cambier, C.J., Kevin K. Takaki, Ryan Larson, et al.. (2013). Mycobacteria manipulate macrophage recruitment through coordinated use of membrane lipids. Nature. 505(7482). 218–222. 353 indexed citations
15.
Tobin, David M. & Lalita Ramakrishnan. (2013). TB: the Yin and Yang of lipid mediators. Current Opinion in Pharmacology. 13(4). 641–645. 44 indexed citations
16.
Tobin, David M., Francisco J. Roca, Sungwhan F. Oh, et al.. (2012). Host Genotype-Specific Therapies Can Optimize the Inflammatory Response to Mycobacterial Infections. Cell. 148(3). 434–446. 420 indexed citations breakdown →
17.
Tobin, David M., Robin C. May, & Robert T. Wheeler. (2012). Zebrafish: A See-Through Host and a Fluorescent Toolbox to Probe Host–Pathogen Interaction. PLoS Pathogens. 8(1). e1002349–e1002349. 78 indexed citations
18.
Tobin, David M., Jay C. Vary, John Ray, et al.. (2010). The lta4h Locus Modulates Susceptibility to Mycobacterial Infection in Zebrafish and Humans. Cell. 140(5). 717–730. 404 indexed citations
19.
Tobin, David M. & Cornelia I. Bargmann. (2004). Invertebrate nociception: Behaviors, neurons and molecules. Journal of Neurobiology. 61(1). 161–174. 64 indexed citations
20.
Tobin, David M., David M. Madsen, Amanda Kahn-Kirby, et al.. (2002). Combinatorial Expression of TRPV Channel Proteins Defines Their Sensory Functions and Subcellular Localization in C. elegans Neurons. Neuron. 35(2). 307–318. 349 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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