John P. Guilinger

4.6k total citations · 3 hit papers
8 papers, 3.4k citations indexed

About

John P. Guilinger is a scholar working on Molecular Biology, Genetics and Spectroscopy. According to data from OpenAlex, John P. Guilinger has authored 8 papers receiving a total of 3.4k indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Molecular Biology, 1 paper in Genetics and 1 paper in Spectroscopy. Recurrent topics in John P. Guilinger's work include CRISPR and Genetic Engineering (6 papers), RNA Interference and Gene Delivery (4 papers) and Advanced biosensing and bioanalysis techniques (4 papers). John P. Guilinger is often cited by papers focused on CRISPR and Genetic Engineering (6 papers), RNA Interference and Gene Delivery (4 papers) and Advanced biosensing and bioanalysis techniques (4 papers). John P. Guilinger collaborates with scholars based in United States. John P. Guilinger's co-authors include David R. Liu, David B. Thompson, Vikram Pattanayak, Steven Lin, Jennifer A. Doudna, Enbo Ma, J. Keith Joung, John A. Zuris, Zheng‐Yi Chen and Yilai Shu and has published in prestigious journals such as Nature Biotechnology, Nature Methods and Journal of Medicinal Chemistry.

In The Last Decade

John P. Guilinger

8 papers receiving 3.3k citations

Hit Papers

Cationic lipid-mediated delivery of proteins enables effi... 2013 2026 2017 2021 2014 2013 2014 250 500 750 1000

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
John P. Guilinger United States 8 3.2k 714 399 298 236 8 3.4k
Johnny H. Hu United States 9 2.9k 0.9× 660 0.9× 297 0.7× 226 0.8× 164 0.7× 9 3.1k
Vincent Cascio United States 6 2.8k 0.9× 563 0.8× 288 0.7× 307 1.0× 246 1.0× 7 2.9k
Joshua A. Weinstein United States 10 3.6k 1.1× 729 1.0× 289 0.7× 444 1.5× 324 1.4× 15 4.4k
James K. Nuñez United States 12 3.3k 1.0× 620 0.9× 227 0.6× 253 0.8× 170 0.7× 16 3.6k
Nicolas Wyvekens United States 9 2.5k 0.8× 519 0.7× 355 0.9× 288 1.0× 204 0.9× 11 2.6k
Marie La Russa United States 10 2.7k 0.8× 440 0.6× 210 0.5× 285 1.0× 200 0.8× 14 3.0k
David I. Bryson United States 8 3.1k 0.9× 808 1.1× 257 0.6× 427 1.4× 143 0.6× 10 3.2k
Gang Bao United States 9 4.1k 1.3× 895 1.3× 406 1.0× 520 1.7× 375 1.6× 11 4.5k
Vikram Pattanayak United States 16 4.2k 1.3× 985 1.4× 535 1.3× 461 1.5× 351 1.5× 24 4.4k
Zairan Liu United States 5 2.6k 0.8× 438 0.6× 207 0.5× 242 0.8× 257 1.1× 5 2.8k

Countries citing papers authored by John P. Guilinger

Since Specialization
Citations

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

Fields of papers citing papers by John P. Guilinger

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John P. Guilinger. 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 John P. Guilinger. The network helps show where John P. Guilinger may publish in the future.

Co-authorship network of co-authors of John P. Guilinger

This figure shows the co-authorship network connecting the top 25 collaborators of John P. Guilinger. A scholar is included among the top collaborators of John P. Guilinger 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 John P. Guilinger. John P. Guilinger 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.
McCloskey, Kevin, Eric A. Sigel, Steven Kearnes, et al.. (2020). Machine Learning on DNA-Encoded Libraries: A New Paradigm for Hit Finding. Journal of Medicinal Chemistry. 63(16). 8857–8866. 85 indexed citations
2.
Hubbard, Basil P., Ahmed H. Badran, John A. Zuris, et al.. (2015). Continuous directed evolution of DNA-binding proteins to improve TALEN specificity. Nature Methods. 12(10). 939–942. 89 indexed citations
3.
McDonald, Richard I., et al.. (2014). Electrophilic activity-based RNA probes reveal a self-alkylating RNA for RNA labeling. Nature Chemical Biology. 10(12). 1049–1054. 35 indexed citations
4.
Zuris, John A., David B. Thompson, Yilai Shu, et al.. (2014). Cationic lipid-mediated delivery of proteins enables efficient protein-based genome editing in vitro and in vivo. Nature Biotechnology. 33(1). 73–80. 1160 indexed citations breakdown →
5.
Pattanayak, Vikram, John P. Guilinger, & David R. Liu. (2014). Determining the Specificities of TALENs, Cas9, and Other Genome-Editing Enzymes. Methods in enzymology on CD-ROM/Methods in enzymology. 546. 47–78. 56 indexed citations
6.
Guilinger, John P., Vikram Pattanayak, Deepak Reyon, et al.. (2014). Broad specificity profiling of TALENs results in engineered nucleases with improved DNA-cleavage specificity. Nature Methods. 11(4). 429–435. 163 indexed citations
7.
Guilinger, John P., David B. Thompson, & David R. Liu. (2014). Fusion of catalytically inactive Cas9 to FokI nuclease improves the specificity of genome modification. Nature Biotechnology. 32(6). 577–582. 651 indexed citations breakdown →
8.
Pattanayak, Vikram, Steven Lin, John P. Guilinger, et al.. (2013). High-throughput profiling of off-target DNA cleavage reveals RNA-programmed Cas9 nuclease specificity. Nature Biotechnology. 31(9). 839–843. 1155 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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