Tom Gallagher

4.8k total citations · 2 hit papers
47 papers, 3.2k citations indexed

About

Tom Gallagher is a scholar working on Infectious Diseases, Animal Science and Zoology and Molecular Biology. According to data from OpenAlex, Tom Gallagher has authored 47 papers receiving a total of 3.2k indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Infectious Diseases, 25 papers in Animal Science and Zoology and 9 papers in Molecular Biology. Recurrent topics in Tom Gallagher's work include SARS-CoV-2 and COVID-19 Research (31 papers), Animal Virus Infections Studies (25 papers) and Viral gastroenteritis research and epidemiology (14 papers). Tom Gallagher is often cited by papers focused on SARS-CoV-2 and COVID-19 Research (31 papers), Animal Virus Infections Studies (25 papers) and Viral gastroenteritis research and epidemiology (14 papers). Tom Gallagher collaborates with scholars based in United States, Germany and Switzerland. Tom Gallagher's co-authors include Stanley Perlman, Taylor Heald‐Sargent, Jincun Zhao, Ana Shulla, Kun Li, Paul B. McCray, Roland R. Rueckert, Gitanjali Subramanya, Michael J. Buchmeier and Enya Qing and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and Nature Communications.

In The Last Decade

Tom Gallagher

46 papers receiving 3.1k citations

Hit Papers

A Transmembrane Serine Protease Is Linked to the Severe A... 2010 2026 2015 2020 2010 2014 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tom Gallagher United States 27 2.3k 992 662 380 375 47 3.2k
Peter J. Bredenbeek Netherlands 29 2.8k 1.2× 1.4k 1.4× 1.0k 1.6× 484 1.3× 455 1.2× 49 4.3k
Fernando Almazán Spain 34 2.6k 1.1× 1.5k 1.5× 1.0k 1.5× 309 0.8× 332 0.9× 68 4.2k
Stanley G. Sawicki United States 33 2.6k 1.1× 1.4k 1.4× 897 1.4× 422 1.1× 319 0.9× 54 3.8k
Monique H. Verheije Netherlands 31 1.8k 0.8× 1.2k 1.2× 674 1.0× 881 2.3× 356 0.9× 62 3.1k
Wataru Kamitani Japan 27 2.0k 0.9× 667 0.7× 704 1.1× 600 1.6× 375 1.0× 68 3.0k
Kumari G. Lokugamage United States 23 2.7k 1.1× 759 0.8× 822 1.2× 228 0.6× 524 1.4× 35 3.3k
Sonia Zúñiga Spain 24 2.1k 0.9× 1.3k 1.3× 707 1.1× 150 0.4× 250 0.7× 47 2.7k
Michelle M. Becker United States 14 1.6k 0.7× 568 0.6× 642 1.0× 404 1.1× 458 1.2× 16 2.4k
Cheng Huang United States 24 2.5k 1.1× 721 0.7× 724 1.1× 280 0.7× 532 1.4× 52 3.2k
Paul S. Masters United States 44 4.6k 2.0× 3.0k 3.0× 1.2k 1.8× 651 1.7× 495 1.3× 83 6.1k

Countries citing papers authored by Tom Gallagher

Since Specialization
Citations

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

Fields of papers citing papers by Tom Gallagher

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tom Gallagher

This figure shows the co-authorship network connecting the top 25 collaborators of Tom Gallagher. A scholar is included among the top collaborators of Tom Gallagher 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 Tom Gallagher. Tom Gallagher 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.
Gallagher, Tom. (2024). Neuropilin invites reoviruses into neurons. Cell Host & Microbe. 32(6). 945–946.
2.
Qing, Enya, et al.. (2024). SARS-CoV-2 Omicron variations reveal mechanisms controlling cell entry dynamics and antibody neutralization. PLoS Pathogens. 20(12). e1012757–e1012757. 2 indexed citations
3.
Lei, Ruipeng, Enya Qing, Abby Odle, et al.. (2024). Functional and antigenic characterization of SARS-CoV-2 spike fusion peptide by deep mutational scanning. Nature Communications. 15(1). 4056–4056. 7 indexed citations
4.
Li, Pengfei, Miguel Ortiz, Nicholas Schnicker, et al.. (2024). Adaptation of SARS-CoV-2 to ACE2 H353K mice reveals new spike residues that drive mouse infection. Journal of Virology. 98(1). e0151023–e0151023. 2 indexed citations
5.
Mentrup, Torben, et al.. (2023). Dynamic association of the intramembrane proteases SPPL2a/b and their substrates with tetraspanin-enriched microdomains. iScience. 26(10). 107819–107819. 3 indexed citations
6.
Dey, Debajit, Enya Qing, Yanan He, et al.. (2023). A single C-terminal residue controls SARS-CoV-2 spike trafficking and incorporation into VLPs. Nature Communications. 14(1). 8358–8358. 8 indexed citations
7.
Qing, Enya, et al.. (2023). A cell-free platform to measure coronavirus membrane fusion. STAR Protocols. 4(2). 102189–102189. 4 indexed citations
8.
Gallagher, Tom. (2021). COVID19 therapeutics: Expanding the antiviral arsenal. EBioMedicine. 66. 103289–103289. 3 indexed citations
9.
Qing, Enya, et al.. (2019). Evaluating MERS-CoV Entry Pathways. Methods in molecular biology. 2099. 9–20. 30 indexed citations
10.
Li, Kun, Christine Wohlford-Lenane, Rudragouda Channappanavar, et al.. (2017). Mouse-adapted MERS coronavirus causes lethal lung disease in human DPP4 knockin mice. Proceedings of the National Academy of Sciences. 114(15). E3119–E3128. 137 indexed citations
11.
Park, Jung-Eun & Tom Gallagher. (2017). Lipidation increases antiviral activities of coronavirus fusion-inhibiting peptides. Virology. 511. 9–18. 24 indexed citations
12.
Earnest, James T., Michael P. Hantak, Kun Li, et al.. (2017). The tetraspanin CD9 facilitates MERS-coronavirus entry by scaffolding host cell receptors and proteases. PLoS Pathogens. 13(7). e1006546–e1006546. 113 indexed citations
13.
Gallagher, Tom, et al.. (2017). Inhibition of type I interferon responses by adenovirus serotype-dependent Gas6 binding. Virology. 515. 150–157. 12 indexed citations
14.
Zhao, Jincun, Kun Li, Christine Wohlford-Lenane, et al.. (2014). Rapid generation of a mouse model for Middle East respiratory syndrome. Proceedings of the National Academy of Sciences. 111(13). 4970–4975. 346 indexed citations breakdown →
15.
Hussain, Snawar & Tom Gallagher. (2010). SARS-coronavirus protein 6 conformations required to impede protein import into the nucleus. Virus Research. 153(2). 299–304. 11 indexed citations
16.
Shulla, Ana & Tom Gallagher. (2009). Role of Spike Protein Endodomains in Regulating Coronavirus Entry. Journal of Biological Chemistry. 284(47). 32725–32734. 50 indexed citations
17.
Pasupuleti, Visweswara Rao & Tom Gallagher. (1998). Mouse Hepatitis Virus Receptor Levels Influence Virus-Induced Cytopathology. Advances in experimental medicine and biology. 440. 549–555. 4 indexed citations
18.
Gallagher, Tom, Michael J. Buchmeier, & Stanley Perlman. (1994). Dissemination of MHV4 (Strain JHM) Infection Does Not Require Specific Coronavirus Receptors. Advances in experimental medicine and biology. 342. 279–284. 8 indexed citations
19.
Schneemann, Anette, W Zhong, Tom Gallagher, & Roland R. Rueckert. (1992). Maturation cleavage required for infectivity of a nodavirus. Journal of Virology. 66(11). 6728–6734. 110 indexed citations
20.
Hosur, M.V., Thomas Schmidt, John E. Johnson, et al.. (1987). Structure of an insect virus at 3.0 Å resolution. Proteins Structure Function and Bioinformatics. 2(3). 167–176. 87 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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