Thomas Sheffield

604 total citations
18 papers, 397 citations indexed

About

Thomas Sheffield is a scholar working on Oncology, Molecular Biology and Computational Theory and Mathematics. According to data from OpenAlex, Thomas Sheffield has authored 18 papers receiving a total of 397 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Oncology, 3 papers in Molecular Biology and 3 papers in Computational Theory and Mathematics. Recurrent topics in Thomas Sheffield's work include Computational Drug Discovery Methods (3 papers), Cancer Immunotherapy and Biomarkers (3 papers) and Gene expression and cancer classification (3 papers). Thomas Sheffield is often cited by papers focused on Computational Drug Discovery Methods (3 papers), Cancer Immunotherapy and Biomarkers (3 papers) and Gene expression and cancer classification (3 papers). Thomas Sheffield collaborates with scholars based in United States. Thomas Sheffield's co-authors include Richard Judson, Derik E. Haggard, Logan J. Everett, Clinton Willis, Russell S. Thomas, Joshua Harrill, Joseph L. Bundy, Imran Shah, Benno Rumpf and Yang Xie and has published in prestigious journals such as Environmental Science & Technology, Bioinformatics and Hypertension.

In The Last Decade

Thomas Sheffield

18 papers receiving 388 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Thomas Sheffield United States 11 99 94 74 64 53 18 397
Annie Lumen United States 16 179 1.8× 225 2.4× 24 0.3× 104 1.6× 37 0.7× 29 773
Yu‐Syuan Luo United States 17 113 1.1× 228 2.4× 41 0.6× 18 0.3× 37 0.7× 44 615
Qinchang Chen China 16 240 2.4× 123 1.3× 58 0.8× 103 1.6× 6 0.1× 48 641
Sandrine Micallef France 10 96 1.0× 59 0.6× 15 0.2× 68 1.1× 15 0.3× 24 678
Amy G. Aslamkhan United States 14 171 1.7× 113 1.2× 47 0.6× 115 1.8× 13 0.2× 17 631
Gregory S. Gorman United States 13 191 1.9× 246 2.6× 10 0.1× 85 1.3× 15 0.3× 36 706
Jennifer C. Sasaki United States 11 112 1.1× 193 2.1× 19 0.3× 31 0.5× 4 0.1× 17 551
Don Truong Canada 7 152 1.5× 33 0.4× 17 0.2× 21 0.3× 4 0.1× 7 502
Hongzhi Li China 14 263 2.7× 72 0.8× 29 0.4× 57 0.9× 2 0.0× 39 619
M Kuschner United States 15 78 0.8× 211 2.2× 10 0.1× 39 0.6× 13 0.2× 29 778

Countries citing papers authored by Thomas Sheffield

Since Specialization
Citations

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

Fields of papers citing papers by Thomas Sheffield

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Thomas Sheffield

This figure shows the co-authorship network connecting the top 25 collaborators of Thomas Sheffield. A scholar is included among the top collaborators of Thomas Sheffield 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 Thomas Sheffield. Thomas Sheffield 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.
Sheffield, Thomas, Anupama Sinha, Zhuoming Liu, et al.. (2025). Nanobody screening and machine learning guided identification of cross-variant anti-SARS-CoV-2 neutralizing heavy-chain only antibodies. PLoS Pathogens. 21(1). e1012903–e1012903. 1 indexed citations
2.
Itzstein, Mitchell S. von, Yiqing Wang, David Hsiehchen, et al.. (2024). Highly variable timing renders immunotherapy efficacy and toxicity impractical biomarkers of one another in clinical practice. Frontiers in Immunology. 15. 1351739–1351739. 2 indexed citations
3.
Itzstein, Mitchell S. von, Yiqing Wang, Thomas Sheffield, et al.. (2022). Divergent prognostic effects of pre-existing and treatment-emergent thyroid dysfunction in patients treated with immune checkpoint inhibitors. Cancer Immunology Immunotherapy. 71(9). 2169–2181. 24 indexed citations
4.
Sheffield, Thomas, et al.. (2021). tcplfit2: an R-language general purpose concentration–response modeling package. Bioinformatics. 38(4). 1157–1158. 27 indexed citations
5.
Velasco, Ferdinand, Donghan M. Yang, Thomas Sheffield, et al.. (2021). Association of Healthcare Access With Intensive Care Unit Utilization and Mortality in Patients of Hispanic Ethnicity Hospitalized With COVID‐19. Journal of Hospital Medicine. 16(11). 659–666. 17 indexed citations
6.
Gerber, David E., Thomas Sheffield, Muhammad Shaalan Beg, et al.. (2021). Experience, Perceptions, and Recommendations Concerning COVID-19–Related Clinical Research Adjustments. Journal of the National Comprehensive Cancer Network. 19(5). 505–512. 12 indexed citations
7.
Harrill, Joshua, Logan J. Everett, Derik E. Haggard, et al.. (2021). High-Throughput Transcriptomics Platform for Screening Environmental Chemicals. Toxicological Sciences. 181(1). 68–89. 120 indexed citations
8.
Itzstein, Mitchell S. von, Thomas Sheffield, Shaheen Khan, et al.. (2021). Association between body mass index, dosing strategy, and efficacy of immune checkpoint inhibitors. Journal for ImmunoTherapy of Cancer. 9(6). e002349–e002349. 30 indexed citations
9.
Sheffield, Thomas, Xiaowei Zhan, Qiwei Li, et al.. (2020). Spatial molecular profiling: platforms, applications and analysis tools. Briefings in Bioinformatics. 22(3). 27 indexed citations
10.
Sheffield, Thomas & Richard Judson. (2019). Ensemble QSAR Modeling to Predict Multispecies Fish Toxicity Points of Departure. Figshare. 1 indexed citations
11.
Sheffield, Thomas & Richard Judson. (2019). Ensemble QSAR Modeling to Predict Multispecies Fish Toxicity Lethal Concentrations and Points of Departure. Environmental Science & Technology. 53(21). 12793–12802. 46 indexed citations
12.
Pham, Ly, Thomas Sheffield, Prachi Pradeep, et al.. (2019). Estimating uncertainty in the context of new approach methodologies for potential use in chemical safety evaluation. Current Opinion in Toxicology. 15. 40–47. 16 indexed citations
13.
Sheffield, Thomas & Benno Rumpf. (2017). Ensemble dynamics and the emergence of correlations in one- and two-dimensional wave turbulence. Physical review. E. 95(6). 62225–62225. 6 indexed citations
14.
Rumpf, Benno & Thomas Sheffield. (2015). Transition of weak wave turbulence to wave turbulence with intermittent collapses. Physical Review E. 92(2). 22927–22927. 11 indexed citations
15.
Taylor, B. J., et al.. (2015). Simulated bi-SQUID Arrays Performing Direction Finding. 3 indexed citations
16.
Quarles, C. A., et al.. (2009). Application of Positron Doppler Broadening Spectroscopy to the Measurement of the Uniformity of Composite Materials. AIP conference proceedings. 940–943. 1 indexed citations
17.
Kostis, John B., Ronald J. Prineas, J. David Curb, et al.. (1991). Systolic Hypertension in the Elderly Program (SHEP). Part 8: Electrocardiographic characteristics.. Hypertension. 17(3_supplement). II123–51. 10 indexed citations
18.
Berger, Rachel L., K B Davis, George C. Kaiser, et al.. (1981). Preservation of the myocardium during coronary artery bypass grafting.. PubMed. 64(2 Pt 2). II61–6. 43 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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