Eric T. Williams

687 citations
17 papers · 432 · h-index 9

Impact in

Papers in

    • Metabolomics and Mass Spectrometry Studies 2
    • Cholinesterase and Neurodegenerative Diseases 5
    • Pharmacogenetics and Drug Metabolism 5

Eric T. Williams

17 papers receiving 426 citations

Peers

Eric T. Williams
Comparison fields: 5 of 83
  • Pharmacology 129
  • Pharmacology 169
  • Computational Theory and Mathematics 83
  • Toxicology 14
  • Cardiology and Cardiovascular Medicine 80
Replace Yasushi Yoshigae with:
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Jean-François Lévesque Canada
James B. Mangold United States
Xuefeng Xiao China
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Eric T. Williams relative to Yasushi Yoshigae Japan Yasushi Yoshigae's profile →
Citations per field
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Citations per year

Countries citing papers authored by Eric T. Williams

Since Specialization
Citations

This map shows the geographic impact of Eric T. Williams'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 Eric T. Williams with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eric T. Williams more than expected).

Fields of papers citing papers by Eric T. Williams

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Eric T. Williams. 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 Eric T. Williams. The network helps show where Eric T. Williams may publish in the future.

Co-authors

The 25 scholars most cited alongside Eric T. Williams, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Eric T. Williams Line = papers co-authored together Eric T. Williams links everyone, so they are left out of the graph.

All Works

17 of 17 papers shown
#Work
1 2011107
2 2008105
3 201161
4 200439
5 201028
6 200722
7 201121
8 200415
9 20138
10 20208
11 20204
12 20223
13 20233
14 20073
15 20213
16
Chimpanzee: A predictive model for the human cytochrome P450 3A subfamily
20061
17 20191

About Eric T. Williams

Eric T. Williams is a scholar working on Molecular Biology, Pharmacology, Pharmacology, Computational Theory and Mathematics and Radiology, Nuclear Medicine and Imaging, having authored 17 papers that have together received 432 indexed citations. Recurring topics across this work include Cholinesterase and Neurodegenerative Diseases (5 papers), Pharmacogenetics and Drug Metabolism (5 papers), Computational Drug Discovery Methods (4 papers), Monoclonal and Polyclonal Antibodies Research (4 papers), Toxin Mechanisms and Immunotoxins (3 papers), Pharmacological Effects and Toxicity Studies (2 papers), HER2/EGFR in Cancer Research (2 papers) and Metabolomics and Mass Spectrometry Studies (2 papers). The work is most often cited by research in Pharmacology (129 citations), Pharmacology (169 citations), Computational Theory and Mathematics (83 citations), Toxicology (14 citations) and Cardiology and Cardiovascular Medicine (80 citations). Eric T. Williams has collaborated with scholars based in United States, Switzerland and United Kingdom. Frequent co-authors include Steven Wrighton, E.J. Perkins, Y. Nancy Wong, Christopher Patten, Jie Wang, Kenneth J. Ruterbories, Yuewei Qian, G. Douglas Ponsler, Miho Kazui and Nagy A. Farid. Their work appears in journals such as Drug Metabolism and Disposition, Cancer Research, Molecular Phylogenetics and Evolution, Comparative Biochemistry and Physiology Part D Genomics and Proteomics and Journal of Pharmacology and Experimental Therapeutics.

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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