Tracey Williams

554 total citations
18 papers, 354 citations indexed

About

Tracey Williams is a scholar working on Molecular Biology, Oncology and Insect Science. According to data from OpenAlex, Tracey Williams has authored 18 papers receiving a total of 354 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Molecular Biology, 6 papers in Oncology and 4 papers in Insect Science. Recurrent topics in Tracey Williams's work include HER2/EGFR in Cancer Research (3 papers), Computational Drug Discovery Methods (2 papers) and Genetics, Aging, and Longevity in Model Organisms (2 papers). Tracey Williams is often cited by papers focused on HER2/EGFR in Cancer Research (3 papers), Computational Drug Discovery Methods (2 papers) and Genetics, Aging, and Longevity in Model Organisms (2 papers). Tracey Williams collaborates with scholars based in United States, United Kingdom and Canada. Tracey Williams's co-authors include Adeera Levin, Gordon E. Pate, John G. Webb, Stephen Shalansky, Karin H. Humphries, Christopher E. Buller, Debra J. Woods, Timothy G. Geary, Sally M. Williamson and Adrian J. Wolstenholme and has published in prestigious journals such as Proceedings of the National Academy of Sciences, PLoS Pathogens and Biochemical Pharmacology.

In The Last Decade

Tracey Williams

18 papers receiving 338 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tracey Williams United States 9 113 61 52 48 39 18 354
Sharron L. O’Neill United States 11 15 0.1× 26 0.4× 15 0.3× 51 1.1× 77 2.0× 20 338
Sandra S. Snook United States 11 55 0.5× 107 1.8× 8 0.2× 20 0.4× 192 4.9× 19 473
Joyce G. Slusser United States 9 69 0.6× 75 1.2× 7 0.1× 3 0.1× 27 0.7× 15 372
Samuel Thomas United States 10 20 0.2× 186 3.0× 17 0.3× 8 0.2× 43 1.1× 20 434
Matthias Schulz Germany 11 16 0.1× 53 0.9× 29 0.6× 7 0.1× 23 0.6× 18 620
Ingela Nilsson Sweden 11 7 0.1× 58 1.0× 8 0.2× 3 0.1× 37 0.9× 19 367
Anna Ulrich United Kingdom 10 4 0.0× 83 1.4× 23 0.4× 30 0.6× 40 1.0× 14 341
J. Thompson United Kingdom 8 7 0.1× 187 3.1× 6 0.1× 154 3.2× 9 0.2× 21 510
Clarice Sampaio Alho Brazil 16 21 0.2× 379 6.2× 44 0.8× 3 0.1× 39 1.0× 55 699
Maria G. Detsika Greece 11 15 0.1× 117 1.9× 10 0.2× 47 1.2× 36 304

Countries citing papers authored by Tracey Williams

Since Specialization
Citations

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

Fields of papers citing papers by Tracey Williams

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tracey Williams

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

All Works

18 of 18 papers shown
1.
Tsang, Erica S., Velda X. Han, Tracey Williams, et al.. (2024). Ketogenic diet modifies ribosomal protein dysregulation in KMT2D Kabuki syndrome. EBioMedicine. 104. 105156–105156. 7 indexed citations
2.
Nambulli, Sham, Linda J. Rennick, Natasha L. Tilston‐Lunel, et al.. (2022). FeMV is a cathepsin-dependent unique morbillivirus infecting the kidneys of domestic cats. Proceedings of the National Academy of Sciences. 119(43). e2209405119–e2209405119. 8 indexed citations
3.
Subramanian, Govindan, Paul D. Johnson, Tracey Williams, et al.. (2021). In Pursuit of an Allosteric Human Tropomyosin Kinase A (hTrkA) Inhibitor for Chronic Pain. ACS Medicinal Chemistry Letters. 12(11). 1847–1852. 8 indexed citations
4.
Subramanian, Govindan, et al.. (2020). Synthetic inhibitor leads of human tropomyosin receptor kinase A (hTrkA). RSC Medicinal Chemistry. 11(3). 370–377. 3 indexed citations
5.
Subramanian, Govindan, Yaqi Zhu, Julie White, et al.. (2019). Lead identification and characterization of hTrkA type 2 inhibitors. Bioorganic & Medicinal Chemistry Letters. 29(22). 126680–126680. 5 indexed citations
6.
Subramanian, Govindan, et al.. (2019). Type 2 inhibitor leads of human tropomyosin receptor kinase (hTrkA). Bioorganic & Medicinal Chemistry Letters. 29(19). 126624–126624. 5 indexed citations
7.
Subramanian, Govindan, Paul D. Johnson, Yaqi Zhu, et al.. (2019). Deciphering the Allosteric Binding Mechanism of the Human Tropomyosin Receptor Kinase A (hTrkA) Inhibitors. ACS Chemical Biology. 14(6). 1205–1216. 15 indexed citations
8.
Sharp, Alana, et al.. (2015). The opossum MHC genomic region revisited. Immunogenetics. 67(4). 259–264. 6 indexed citations
9.
Larsen, Martha J., et al.. (2012). Functional expression and characterization of the C. elegans G-protein-coupled FLP-2 Receptor (T19F4.1) in mammalian cells and yeast. International Journal for Parasitology Drugs and Drug Resistance. 3. 1–7. 12 indexed citations
10.
Woods, Debra J., et al.. (2010). Receptor-Based Discovery Strategies for Insecticides and Parasiticides: A Review. Advances in experimental medicine and biology. 692. 1–9. 10 indexed citations
11.
Woods, Debra J., et al.. (2010). Receptor-Based Discovery Strategies for Insecticides and Parasiticides:. 1 indexed citations
12.
Williamson, Sally M., Alan P. Robertson, Laurence A. Brown, et al.. (2009). The Nicotinic Acetylcholine Receptors of the Parasitic Nematode Ascaris suum: Formation of Two Distinct Drug Targets by Varying the Relative Expression Levels of Two Subunits. PLoS Pathogens. 5(7). e1000517–e1000517. 65 indexed citations
13.
Bennett, Hayley M., Sally M. Williamson, Alan P. Robertson, et al.. (2009). The nicotinic acetylcholine receptors of Ascaris suum. Biochemical Pharmacology. 78(7). 899–900. 1 indexed citations
14.
Howell, Melanie, et al.. (2005). Herpesvirus pan encodes a functional homologue of BHRF1, the Epstein-Barr virus v-Bcl-2.. BMC Microbiology. 5(1). 6–6. 9 indexed citations
15.
Williams, Tracey, et al.. (2005). Nematode neuropeptide receptors and their development as anthelmintic screens. Parasitology. 131(S1). S169–S177. 26 indexed citations
16.
Webb, John G., Gordon E. Pate, Karin H. Humphries, et al.. (2004). A randomized controlled trial of intravenous N-acetylcysteine for the prevention of contrast-induced nephropathy after cardiac catheterization: Lack of effect. American Heart Journal. 148(3). 422–429. 128 indexed citations
17.
Williams, Tracey, et al.. (2001). BHRF1 Is Highly Conserved in Primate Virus Analogues of Epstein-Barr Virus. Intervirology. 44(1). 55–58. 13 indexed citations
18.
Sampson, Michael, Tracey Williams, P. J. Heyburn, et al.. (2000). Prevalence of HFE (hemochromatosis gene) mutations in unselected male patients with type 2 diabetes. Journal of Laboratory and Clinical Medicine. 135(2). 170–173. 32 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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