Linyi Gao

7.1k total citations · 4 hit papers
22 papers, 4.1k citations indexed

About

Linyi Gao is a scholar working on Molecular Biology, Ecology and Genetics. According to data from OpenAlex, Linyi Gao has authored 22 papers receiving a total of 4.1k indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Molecular Biology, 5 papers in Ecology and 3 papers in Genetics. Recurrent topics in Linyi Gao's work include CRISPR and Genetic Engineering (12 papers), RNA and protein synthesis mechanisms (9 papers) and Bacteriophages and microbial interactions (5 papers). Linyi Gao is often cited by papers focused on CRISPR and Genetic Engineering (12 papers), RNA and protein synthesis mechanisms (9 papers) and Bacteriophages and microbial interactions (5 papers). Linyi Gao collaborates with scholars based in United States, Japan and Sweden. Linyi Gao's co-authors include Feng Zhang, Winston X. Yan, Bernd Zetsche, David Scott, Ian M. Slaymaker, Kira S. Makarova, Eugene V. Koonin, Jonathan L. Schmid‐Burgk, Jonathan Strecker and Nicola Crosetto and has published in prestigious journals such as Nature, Science and Nucleic Acids Research.

In The Last Decade

Linyi Gao

22 papers receiving 4.0k citations

Hit Papers

Rationally engineered Cas9 nucleases with improved specif... 2015 2026 2018 2022 2015 2016 2020 2022 500 1000 1.5k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Linyi Gao United States 15 3.5k 692 447 388 324 22 4.1k
James K. Nuñez United States 12 3.3k 0.9× 620 0.9× 253 0.6× 227 0.6× 175 0.5× 16 3.6k
Sy Redding United States 14 4.1k 1.2× 492 0.7× 502 1.1× 153 0.4× 128 0.4× 17 4.3k
Matthew H. Larson United States 14 8.2k 2.4× 2.0k 2.8× 658 1.5× 525 1.4× 396 1.2× 19 8.9k
Randall J. Platt Switzerland 20 3.5k 1.0× 798 1.2× 258 0.6× 203 0.5× 48 0.1× 40 4.5k
Gavin J. Knott United States 21 3.5k 1.0× 504 0.7× 385 0.9× 327 0.8× 206 0.6× 36 3.9k
Vanessa K. Verdine United States 5 4.3k 1.2× 575 0.8× 378 0.8× 327 0.8× 166 0.5× 6 4.8k
Joshua A. Weinstein United States 10 3.6k 1.0× 729 1.1× 444 1.0× 289 0.7× 55 0.2× 15 4.4k
Joerg Schnitzbauer United States 11 2.0k 0.6× 246 0.4× 211 0.5× 110 0.3× 124 0.4× 12 2.6k
Luhan Yang United States 6 7.3k 2.1× 1.7k 2.5× 804 1.8× 556 1.4× 101 0.3× 8 8.0k
Marie La Russa United States 10 2.7k 0.8× 440 0.6× 285 0.6× 210 0.5× 64 0.2× 14 3.0k

Countries citing papers authored by Linyi Gao

Since Specialization
Citations

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

Fields of papers citing papers by Linyi Gao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Linyi Gao

This figure shows the co-authorship network connecting the top 25 collaborators of Linyi Gao. A scholar is included among the top collaborators of Linyi Gao 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 Linyi Gao. Linyi Gao 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.
Kretsch, Rachael C., Svetlana A. Shabalina, Eugene V. Koonin, et al.. (2025). Naturally ornate RNA-only complexes revealed by cryo-EM. Nature. 643(8073). 1135–1142. 3 indexed citations
2.
Wilkinson, Max E., David Li, Linyi Gao, Rhiannon K. Macrae, & Feng Zhang. (2024). Phage-triggered reverse transcription assembles a toxic repetitive gene from a noncoding RNA. Science. 386(6717). eadq3977–eadq3977. 21 indexed citations
3.
Gao, Linyi, et al.. (2023). Fine-grained image recognition method for digital media based on feature enhancement strategy. Neural Computing and Applications. 36(5). 2323–2335. 3 indexed citations
4.
Mestre, Mario Rodríguez, Linyi Gao, Shiraz A. Shah, et al.. (2022). UG/Abi: a highly diverse family of prokaryotic reverse transcriptases associated with defense functions. Nucleic Acids Research. 50(11). 6084–6101. 22 indexed citations
5.
Gao, Linyi, Max E. Wilkinson, Jonathan Strecker, et al.. (2022). Prokaryotic innate immunity through pattern recognition of conserved viral proteins. Science. 377(6607). eabm4096–eabm4096. 138 indexed citations breakdown →
6.
Gao, Ruixuan, Chih-Chieh Yu, Linyi Gao, et al.. (2021). A highly homogeneous polymer composed of tetrahedron-like monomers for high-isotropy expansion microscopy. Nature Nanotechnology. 16(6). 698–707. 49 indexed citations
7.
Gao, Linyi, Han Altae-Tran, Kira S. Makarova, et al.. (2020). Diverse enzymatic activities mediate antiviral immunity in prokaryotes. Science. 369(6507). 1077–1084. 342 indexed citations breakdown →
8.
Schmid‐Burgk, Jonathan L., Linyi Gao, David Li, et al.. (2020). Highly Parallel Profiling of Cas9 Variant Specificity. Molecular Cell. 78(4). 794–800.e8. 145 indexed citations
9.
Altae-Tran, Han, et al.. (2020). Computational Identification of Repeat-Containing Proteins and Systems. SHILAP Revista de lepidopterología. 1. e10–e10. 3 indexed citations
10.
Strecker, Jonathan, Sara R. Jones, Balwina Koopal, et al.. (2019). Engineering of CRISPR-Cas12b for human genome editing. Nature Communications. 10(1). 212–212. 266 indexed citations
12.
Tekin, Halil, Sean Simmons, Beryl B. Cummings, et al.. (2018). Effects of 3D culturing conditions on the transcriptomic profile of stem-cell-derived neurons. Nature Biomedical Engineering. 2(7). 540–554. 72 indexed citations
13.
Gao, Linyi, David Cox, Winston X. Yan, et al.. (2017). Engineered Cpf1 variants with altered PAM specificities. PMC. 1 indexed citations
14.
Mirzazadeh, Reza, Silvano Garnerone, Martin Schneider, et al.. (2017). BLISS is a versatile and quantitative method for genome-wide profiling of DNA double-strand breaks. Nature. 86 indexed citations
15.
Yan, Winston X., Reza Mirzazadeh, Silvano Garnerone, et al.. (2017). BLISS is a versatile and quantitative method for genome-wide profiling of DNA double-strand breaks. Nature Communications. 8(1). 15058–15058. 272 indexed citations
16.
Gao, Linyi, David Cox, Winston X. Yan, et al.. (2017). Engineered Cpf1 variants with altered PAM specificities. Nature Biotechnology. 35(8). 789–792. 335 indexed citations
17.
Nishimasu, Hiroshi, Takashi Yamano, Linyi Gao, et al.. (2017). Structural Basis for the Altered PAM Recognition by Engineered CRISPR-Cpf1. Molecular Cell. 67(1). 139–147.e2. 78 indexed citations
18.
Tillberg, Paul W., Fei Chen, Kiryl D. Piatkevich, et al.. (2016). Protein-retention expansion microscopy of cells and tissues labeled using standard fluorescent proteins and antibodies. PMC. 2 indexed citations
19.
Tillberg, Paul W., Fei Chen, Kiryl D. Piatkevich, et al.. (2016). Protein-retention expansion microscopy of cells and tissues labeled using standard fluorescent proteins and antibodies. Nature Biotechnology. 34(9). 987–992. 419 indexed citations breakdown →
20.
Slaymaker, Ian M., Linyi Gao, Bernd Zetsche, et al.. (2015). Rationally engineered Cas9 nucleases with improved specificity. Science. 351(6268). 84–88. 1786 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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