Jeffrey Velasquez

1.6k total citations · 1 hit paper
8 papers, 1.3k citations indexed

About

Jeffrey Velasquez is a scholar working on Molecular Biology, Infectious Diseases and Spectroscopy. According to data from OpenAlex, Jeffrey Velasquez has authored 8 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Molecular Biology, 2 papers in Infectious Diseases and 2 papers in Spectroscopy. Recurrent topics in Jeffrey Velasquez's work include SARS-CoV-2 and COVID-19 Research (2 papers), Viral Infectious Diseases and Gene Expression in Insects (2 papers) and Receptor Mechanisms and Signaling (2 papers). Jeffrey Velasquez is often cited by papers focused on SARS-CoV-2 and COVID-19 Research (2 papers), Viral Infectious Diseases and Gene Expression in Insects (2 papers) and Receptor Mechanisms and Signaling (2 papers). Jeffrey Velasquez collaborates with scholars based in United States, China and Russia. Jeffrey Velasquez's co-authors include Raymond C. Stevens, Mark T. Griffith, Peter Kühn, Michael A. Hanson, C. Roth, Ellen Y. T. Chien, Vadim Cherezov, Veli‐Pekka Jaakola, Kin Moy and Kumar Singh Saikatendu and has published in prestigious journals such as Cell, Journal of Virology and Nature Protocols.

In The Last Decade

Jeffrey Velasquez

8 papers receiving 1.3k citations

Hit Papers

A Specific Cholesterol Bi... 2008 2026 2014 2020 2008 250 500 750

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jeffrey Velasquez United States 7 1.0k 417 179 118 112 8 1.3k
Wanchao Yin China 18 673 0.7× 312 0.7× 299 1.7× 210 1.8× 94 0.8× 27 1.1k
Li-Yin Huang United States 8 680 0.7× 365 0.9× 76 0.4× 68 0.6× 83 0.7× 10 802
W S Hill United States 10 1.4k 1.4× 817 2.0× 106 0.6× 65 0.6× 94 0.8× 11 1.7k
Catherine L. Worth Germany 21 1.2k 1.1× 207 0.5× 76 0.4× 210 1.8× 115 1.0× 29 1.6k
R A Dixon United States 8 978 0.9× 398 1.0× 107 0.6× 24 0.2× 52 0.5× 8 1.4k
Kaavya Krishna Kumar United States 14 689 0.7× 394 0.9× 51 0.3× 52 0.4× 94 0.8× 21 991
Tara Mirzadegan United States 16 933 0.9× 301 0.7× 179 1.0× 200 1.7× 134 1.2× 25 1.5k
Irina G. Tikhonova United Kingdom 28 1.6k 1.5× 776 1.9× 36 0.2× 286 2.4× 124 1.1× 97 2.1k
Christian Watson United States 5 554 0.5× 338 0.8× 85 0.5× 94 0.8× 107 1.0× 6 779
Xavier Hanoulle France 23 929 0.9× 56 0.1× 194 1.1× 56 0.5× 146 1.3× 59 1.5k

Countries citing papers authored by Jeffrey Velasquez

Since Specialization
Citations

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

Fields of papers citing papers by Jeffrey Velasquez

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jeffrey Velasquez

This figure shows the co-authorship network connecting the top 25 collaborators of Jeffrey Velasquez. A scholar is included among the top collaborators of Jeffrey Velasquez 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 Jeffrey Velasquez. Jeffrey Velasquez 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.
Audet, Martin, et al.. (2019). Small-scale approach for precrystallization screening in GPCR X-ray crystallography. Nature Protocols. 15(1). 144–160. 5 indexed citations
2.
Audet, Martin, Kate L. White, Billy Breton, et al.. (2018). Crystal structure of misoprostol bound to the labor inducer prostaglandin E2 receptor. Nature Chemical Biology. 15(1). 11–17. 38 indexed citations
3.
Chrencik, Jill, C. Roth, H. Kurata, et al.. (2015). Crystal Structure of Antagonist Bound Human Lysophosphatidic Acid Receptor 1. Cell. 161(7). 1633–1643. 147 indexed citations
4.
Hanson, Michael A., Vadim Cherezov, Mark T. Griffith, et al.. (2008). A Specific Cholesterol Binding Site Is Established by the 2.8 Å Structure of the Human β2-Adrenergic Receptor. Structure. 16(6). 897–905. 780 indexed citations breakdown →
5.
Hanson, Michael A., Alexei Brooun, Kent A. Baker, et al.. (2007). Profiling of membrane protein variants in a baculovirus system by coupling cell-surface detection with small-scale parallel expression. Protein Expression and Purification. 56(1). 85–92. 34 indexed citations
6.
Joseph, Jeremiah S., Kumar Singh Saikatendu, Vanitha Subramanian, et al.. (2006). Crystal Structure of Nonstructural Protein 10 from the Severe Acute Respiratory Syndrome Coronavirus Reveals a Novel Fold with Two Zinc-Binding Motifs. Journal of Virology. 80(16). 7894–7901. 94 indexed citations
7.
Saikatendu, Kumar Singh, Jeremiah S. Joseph, Vanitha Subramanian, et al.. (2005). Structural Basis of Severe Acute Respiratory Syndrome Coronavirus ADP-Ribose-1″-Phosphate Dephosphorylation by a Conserved Domain of nsP3. Structure. 13(11). 1665–1675. 157 indexed citations
8.
Page, Rebecca, Kin Moy, Jeffrey Velasquez, et al.. (2004). Scalable High-Throughput Micro-Expression Device for Recombinant Proteins. BioTechniques. 37(3). 364–370. 26 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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