Paul Shinn

26.0k total citations · 5 hit papers
69 papers, 8.3k citations indexed

About

Paul Shinn is a scholar working on Molecular Biology, Computational Theory and Mathematics and Infectious Diseases. According to data from OpenAlex, Paul Shinn has authored 69 papers receiving a total of 8.3k indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Molecular Biology, 17 papers in Computational Theory and Mathematics and 16 papers in Infectious Diseases. Recurrent topics in Paul Shinn's work include Computational Drug Discovery Methods (17 papers), SARS-CoV-2 and COVID-19 Research (8 papers) and Pharmacogenetics and Drug Metabolism (7 papers). Paul Shinn is often cited by papers focused on Computational Drug Discovery Methods (17 papers), SARS-CoV-2 and COVID-19 Research (8 papers) and Pharmacogenetics and Drug Metabolism (7 papers). Paul Shinn collaborates with scholars based in United States, Canada and United Kingdom. Paul Shinn's co-authors include Joseph R. Ecker, Huaming Chen, Frederic D. Bushman, Charles C. Berry, Christopher P. Austin, Ruili Huang, Matteo Pellegrini, Shawn Cokus, Junshi Yazaki and Xiaoyu Zhang and has published in prestigious journals such as Science, Cell and Proceedings of the National Academy of Sciences.

In The Last Decade

Paul Shinn

68 papers receiving 8.2k citations

Hit Papers

HIV-1 Integration in the Human Genome Favors Active Genes... 2002 2026 2010 2018 2002 2006 2004 2007 2005 400 800 1.2k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. HIV-1 Integration in the Human Genome Favors Active Genes and Local Hotspots
    2002 1.4k citations Paul Shinn, Huaming Chen et al. Cell profile →
  2. Genome-wide High-Resolution Mapping and Functional Analysis of DNA Methylation in Arabidopsis
    2006 1.3k citations Huaming Chen, Paul Shinn et al. Cell profile →
  3. Retroviral DNA Integration: ASLV, HIV, and MLV Show Distinct Target Site Preferences
    2004 737 citations Paul Shinn, Huaming Chen et al. profile →
  4. Common Sequence Polymorphisms Shaping Genetic Diversity in Arabidopsis thaliana
    2007 526 citations Richard M. Clark, Gabriele Schweikert et al. Science profile →
  5. A role for LEDGF/p75 in targeting HIV DNA integration
    2005 504 citations Paul Shinn, Joseph R. Ecker et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Paul Shinn United States 37 4.8k 2.0k 1.7k 1.6k 1.5k 69 8.3k
Giovanni Maga Italy 51 5.5k 1.1× 596 0.3× 517 0.3× 2.2k 1.4× 1.3k 0.9× 270 9.9k
Gilbert Deléage France 35 4.5k 0.9× 1.1k 0.6× 881 0.5× 602 0.4× 257 0.2× 87 7.1k
Rafal Gumienny Switzerland 10 6.2k 1.3× 859 0.4× 1.3k 0.8× 996 0.6× 114 0.1× 10 9.9k
Christine Rempfer Germany 5 5.7k 1.2× 847 0.4× 1.3k 0.8× 991 0.6× 114 0.1× 6 9.3k
Mark N. Wass United Kingdom 25 5.8k 1.2× 1.1k 0.5× 1.6k 0.9× 871 0.5× 106 0.1× 68 9.5k
Ping Wei China 40 4.0k 0.8× 485 0.2× 235 0.1× 1.1k 0.7× 1.1k 0.8× 204 6.3k
Rosalba Lepore Italy 15 5.7k 1.2× 814 0.4× 1.3k 0.7× 954 0.6× 105 0.1× 27 9.3k
Florian Heer Switzerland 3 5.3k 1.1× 798 0.4× 1.2k 0.7× 925 0.6× 104 0.1× 4 8.7k
Lim Heo United States 23 6.1k 1.3× 707 0.3× 667 0.4× 755 0.5× 152 0.1× 38 8.3k
Christopher M. Yates United States 20 5.2k 1.1× 1.1k 0.5× 1.5k 0.9× 1.2k 0.7× 91 0.1× 29 8.8k

Countries citing papers authored by Paul Shinn

Since Specialization
Citations

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

Fields of papers citing papers by Paul Shinn

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Paul Shinn

This figure shows the co-authorship network connecting the top 25 collaborators of Paul Shinn. A scholar is included among the top collaborators of Paul Shinn 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 Paul Shinn. Paul Shinn 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.
Senatorov, Ilya S., Joel M. Bowman, Keith H. Jansson, et al.. (2024). Castrate-resistant prostate cancer response to taxane is determined by an HNF1-dependent apoptosis resistance circuit. Cell Reports Medicine. 5(12). 101868–101868. 2 indexed citations
2.
Hu, Xin, Paul Shinn, Zina Itkin, et al.. (2024). A Comprehensive Collection of Pain and Opioid Use Disorder Compounds for High-Throughput Screening and Artificial Intelligence-Driven Drug Discovery. ACS Pharmacology & Translational Science. 7(8). 2391–2400. 2 indexed citations
3.
Richard, Ann M., Dingyin Tao, Christopher A. LeClair, et al.. (2024). Analytical Quality Evaluation of the Tox21 Compound Library. Chemical Research in Toxicology. 38(1). 15–41. 4 indexed citations
4.
Yasgar, Adam, Richard T. Eastman, Ruili Huang, et al.. (2023). Quantitative Bioactivity Signatures of Dietary Supplements and Natural Products. ACS Pharmacology & Translational Science. 6(5). 683–701. 2 indexed citations
5.
Reinhold, William C., Kelli M. Wilson, Fathi Elloumi, et al.. (2023). CellMinerCDB: NCATS Is a Web-Based Portal Integrating Public Cancer Cell Line Databases for Pharmacogenomic Explorations. Cancer Research. 83(12). 1941–1952. 12 indexed citations
6.
Shrimp, Jonathan H., John Janiszewski, Catherine Z. Chen, et al.. (2022). Suite of TMPRSS2 Assays for Screening Drug Repurposing Candidates as Potential Treatments of COVID-19. ACS Infectious Diseases. 8(6). 1191–1203. 6 indexed citations
7.
Hu, Xin, Jonathan H. Shrimp, Hui Guo, et al.. (2021). Discovery of TMPRSS2 Inhibitors from Virtual Screening as a Potential Treatment of COVID-19. ACS Pharmacology & Translational Science. 4(3). 1124–1135. 44 indexed citations
8.
Hu, Xin, Catherine Z. Chen, Miao Xu, et al.. (2021). Discovery of Small Molecule Entry Inhibitors Targeting the Fusion Peptide of SARS-CoV-2 Spike Protein. ACS Medicinal Chemistry Letters. 12(8). 1267–1274. 16 indexed citations
9.
Zhu, Wei, Miao Xu, Catherine Z. Chen, et al.. (2020). Identification of SARS-CoV-2 3CL Protease Inhibitors by a Quantitative High-Throughput Screening. ACS Pharmacology & Translational Science. 3(5). 1008–1016. 152 indexed citations
10.
Hanson, Quinlin, Kelli M. Wilson, Min Shen, et al.. (2020). Targeting ACE2–RBD Interaction as a Platform for COVID-19 Therapeutics: Development and Drug-Repurposing Screen of an AlphaLISA Proximity Assay. ACS Pharmacology & Translational Science. 3(6). 1352–1360. 63 indexed citations
11.
Bian, Yansong, Yaroslav Teper, Lesley A. Mathews Griner, et al.. (2019). Target Deconvolution of a Multikinase Inhibitor with Antimetastatic Properties Identifies TAOK3 as a Key Contributor to a Cancer Stem Cell–Like Phenotype. Molecular Cancer Therapeutics. 18(11). 2097–2110. 19 indexed citations
12.
Huang, Ruili, Hu Zhu, Paul Shinn, et al.. (2019). The NCATS Pharmaceutical Collection: a 10-year update. Drug Discovery Today. 24(12). 2341–2349. 41 indexed citations
13.
Coussens, Nathan P., Stephen C. Kales, Mark J. Henderson, et al.. (2018). High-throughput screening with nucleosome substrate identifies small-molecule inhibitors of the human histone lysine methyltransferase NSD2. Journal of Biological Chemistry. 293(35). 13750–13765. 55 indexed citations
14.
Tong, Zhi‐Bin, Ruili Huang, Yuhong Wang, et al.. (2017). The Toxmatrix: Chemo-Genomic Profiling Identifies Interactions That Reveal Mechanisms of Toxicity. Chemical Research in Toxicology. 31(2). 127–136. 9 indexed citations
15.
Sun, Wei, Shihua He, Carles Martínez‐Romero, et al.. (2016). Synergistic drug combination effectively blocks Ebola virus infection. Antiviral Research. 137. 165–172. 68 indexed citations
16.
Mathews, Lesley, Jonathan M. Keller, Bonnie L. Goodwin, et al.. (2012). A 1536-Well Quantitative High-Throughput Screen to Identify Compounds Targeting Cancer Stem Cells. SLAS DISCOVERY. 17(9). 1231–1242. 27 indexed citations
17.
Huang, Ruili, Menghang Xia, Ming-Hsuang Cho, et al.. (2011). Chemical Genomics Profiling of Environmental Chemical Modulation of Human Nuclear Receptors. Environmental Health Perspectives. 119(8). 1142–1148. 166 indexed citations
18.
Liu, Ke, Noel Southall, James Inglese, et al.. (2010). A Multiplex Calcium Assay for Identification of GPCR Agonists and Antagonists. Assay and Drug Development Technologies. 8(3). 362–374. 25 indexed citations
19.
Clark, Richard M., Gabriele Schweikert, Christopher Toomajian, et al.. (2007). Common Sequence Polymorphisms Shaping Genetic Diversity in Arabidopsis thaliana. Science. 317(5836). 338–342. 526 indexed citations breakdown →
20.
Shinn, Paul, et al.. (2002). HIV-1 Integration in the Human Genome Favors Active Genes and Local Hotspots. Cell. 110(4). 521–529. 1365 indexed citations breakdown →

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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