Tommy Cathey

673 total citations
8 papers, 467 citations indexed

About

Tommy Cathey is a scholar working on Molecular Biology, Computational Theory and Mathematics and Spectroscopy. According to data from OpenAlex, Tommy Cathey has authored 8 papers receiving a total of 467 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Molecular Biology, 4 papers in Computational Theory and Mathematics and 3 papers in Spectroscopy. Recurrent topics in Tommy Cathey's work include Computational Drug Discovery Methods (4 papers), Metabolomics and Mass Spectrometry Studies (3 papers) and Analytical Chemistry and Chromatography (3 papers). Tommy Cathey is often cited by papers focused on Computational Drug Discovery Methods (4 papers), Metabolomics and Mass Spectrometry Studies (3 papers) and Analytical Chemistry and Chromatography (3 papers). Tommy Cathey collaborates with scholars based in United States, Luxembourg and Denmark. Tommy Cathey's co-authors include Richard Judson, Thomas R. Transue, Ann M. Richard, Matthew T. Martin, Richard M. Spencer, Keith A. Houck, David J. Dix, Fathi Elloumi, David M. Reif and Alicia M. Frame and has published in prestigious journals such as Bioinformatics, Analytical Chemistry and International Journal of Molecular Sciences.

In The Last Decade

Tommy Cathey

8 papers receiving 463 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tommy Cathey United States 7 211 163 156 77 49 8 467
Steve Gutsell United Kingdom 15 216 1.0× 255 1.6× 160 1.0× 122 1.6× 31 0.6× 33 649
Claire M. Ellison United Kingdom 11 158 0.7× 242 1.5× 91 0.6× 77 1.0× 17 0.3× 14 469
Andrea-Nicole Richarz United Kingdom 17 310 1.5× 232 1.4× 102 0.7× 181 2.4× 58 1.2× 28 822
Arianna Bassan Italy 13 135 0.6× 245 1.5× 96 0.6× 71 0.9× 17 0.3× 30 501
Chanita Kuseva Bulgaria 14 151 0.7× 262 1.6× 79 0.5× 164 2.1× 28 0.6× 20 582
James W. Firman United Kingdom 14 131 0.6× 216 1.3× 114 0.7× 117 1.5× 33 0.7× 38 487
Inthirany Thillainadarajah United States 8 346 1.6× 216 1.3× 205 1.3× 85 1.1× 32 0.7× 9 794
Serena Manganelli Italy 13 87 0.4× 262 1.6× 96 0.6× 37 0.5× 28 0.6× 27 484
Shannon Bell United States 13 136 0.6× 133 0.8× 206 1.3× 114 1.5× 25 0.5× 21 531
Cory L Strope United States 7 333 1.6× 135 0.8× 139 0.9× 164 2.1× 32 0.7× 7 590

Countries citing papers authored by Tommy Cathey

Since Specialization
Citations

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

Fields of papers citing papers by Tommy Cathey

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tommy Cathey

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

All Works

8 of 8 papers shown
1.
Chao, Alex, Jeffrey M. Minucci, Rizwan Suliankatchi Abdulkader, et al.. (2025). Prioritizing Chemical Candidates from Non-targeted Analysis Using Metadata, Spectral Similarity, and Hazard Scoring within INTERPRET NTA. Analytical Chemistry. 97(29). 15904–15912. 1 indexed citations
2.
Sobus, Jon R., Alex Chao, Jeffrey M. Minucci, et al.. (2025). Automated QA/QC reporting for non-targeted analysis: a demonstration of “INTERPRET NTA” with de facto water reuse data. Analytical and Bioanalytical Chemistry. 417(9). 1897–1914. 7 indexed citations
3.
Chao, Alex, Andrew D. McEachran, Ilya A. Balabin, et al.. (2020). In silico MS/MS spectra for identifying unknowns: a critical examination using CFM-ID algorithms and ENTACT mixture samples. Analytical and Bioanalytical Chemistry. 412(6). 1303–1315. 41 indexed citations
4.
McEachran, Andrew D., Ilya A. Balabin, Tommy Cathey, et al.. (2019). Linking in silico MS/MS spectra with chemistry data to improve identification of unknowns. Scientific Data. 6(1). 141–141. 35 indexed citations
5.
Dionisio, Kathie L., Alicia M. Frame, John F. Wambaugh, et al.. (2015). Exploring consumer exposure pathways and patterns of use for chemicals in the environment. Toxicology Reports. 2. 228–237. 92 indexed citations
6.
Reif, David M., Eric F. Lock, Fred A. Wright, et al.. (2012). ToxPi GUI: an interactive visualization tool for transparent integration of data from diverse sources of evidence. Bioinformatics. 29(3). 402–403. 71 indexed citations
7.
Judson, Richard, Matthew T. Martin, Peter Egeghy, et al.. (2012). Aggregating Data for Computational Toxicology Applications: The U.S. Environmental Protection Agency (EPA) Aggregated Computational Toxicology Resource (ACToR) System. International Journal of Molecular Sciences. 13(2). 1805–1831. 73 indexed citations
8.
Judson, Richard, Ann M. Richard, David J. Dix, et al.. (2008). ACToR — Aggregated Computational Toxicology Resource. Toxicology and Applied Pharmacology. 233(1). 7–13. 147 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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2026