Jun Fu

8.4k total citations · 1 hit paper
115 papers, 5.9k citations indexed

About

Jun Fu is a scholar working on Molecular Biology, Genetics and Pharmacology. According to data from OpenAlex, Jun Fu has authored 115 papers receiving a total of 5.9k indexed citations (citations by other indexed papers that have themselves been cited), including 78 papers in Molecular Biology, 31 papers in Genetics and 18 papers in Pharmacology. Recurrent topics in Jun Fu's work include CRISPR and Genetic Engineering (32 papers), Bacterial Genetics and Biotechnology (19 papers) and Microbial Natural Products and Biosynthesis (18 papers). Jun Fu is often cited by papers focused on CRISPR and Genetic Engineering (32 papers), Bacterial Genetics and Biotechnology (19 papers) and Microbial Natural Products and Biosynthesis (18 papers). Jun Fu collaborates with scholars based in China, Germany and United States. Jun Fu's co-authors include A. Francis Stewart, A. Francis Stewart, Rolf Müller, Youming Zhang, Konstantinos Anastassiadis, Xiaoying Bian, Youming Zhang, William C. Skarnes, Liqiu Xia and Vivek Iyer and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Nucleic Acids Research.

In The Last Decade

Jun Fu

110 papers receiving 5.9k citations

Hit Papers

A conditional knockout resource for the genome-wide study... 2011 2026 2016 2021 2011 400 800 1.2k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jun Fu China 38 4.4k 1.5k 970 478 441 115 5.9k
A. Francis Stewart Germany 44 5.2k 1.2× 1.5k 1.0× 572 0.6× 384 0.8× 255 0.6× 98 6.2k
A. Francis Stewart Germany 36 4.8k 1.1× 2.4k 1.6× 415 0.4× 377 0.8× 280 0.6× 59 6.2k
Bradley S. Fletcher United States 32 2.4k 0.6× 1.6k 1.1× 2.1k 2.1× 433 0.9× 78 0.2× 71 5.6k
Simon J. McGowan United Kingdom 36 2.8k 0.6× 835 0.6× 189 0.2× 496 1.0× 182 0.4× 67 4.0k
Jeffrey R. de Wet United States 16 4.1k 0.9× 1.4k 0.9× 146 0.2× 638 1.3× 411 0.9× 19 5.4k
Philippe Fort France 46 5.4k 1.2× 954 0.7× 408 0.4× 748 1.6× 95 0.2× 110 8.4k
Yuji Arai Japan 39 3.1k 0.7× 817 0.6× 209 0.2× 1.0k 2.1× 416 0.9× 156 5.4k
Jeffrey Field United States 45 4.8k 1.1× 631 0.4× 249 0.3× 392 0.8× 108 0.2× 87 6.7k
Craig V. Byus United States 30 2.8k 0.6× 516 0.4× 228 0.2× 322 0.7× 196 0.4× 65 4.6k
Yi Liu China 41 3.9k 0.9× 536 0.4× 237 0.2× 1.8k 3.9× 92 0.2× 156 5.9k

Countries citing papers authored by Jun Fu

Since Specialization
Citations

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

Fields of papers citing papers by Jun Fu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jun Fu

This figure shows the co-authorship network connecting the top 25 collaborators of Jun Fu. A scholar is included among the top collaborators of Jun Fu 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 Jun Fu. Jun Fu 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.
Yan, Xiaoling, Xiaoli Zhang, Ling Zhou, et al.. (2025). Effects of SADS‐CoV accessory proteins NS3a, NS7a, and NS7b on viral pathogenicity: A multi‐omics investigation. PubMed. 2(3). e70015–e70015.
2.
Li, Caiming, Weihang Huang, Xia Wang, et al.. (2024). All‐In‐One OsciDrop Digital PCR System for Automated and Highly Multiplexed Molecular Diagnostics. Advanced Science. 11(21). e2309557–e2309557. 14 indexed citations
4.
Fu, Jun, Xiao Liu, Bin Yin, Pengcheng Shu, & Xiaozhong Peng. (2023). NECL2 regulates blood–testis barrier dynamics in mouse testes. Cell and Tissue Research. 392(3). 811–826. 2 indexed citations
6.
Zheng, Wentao, Xue Wang, Shiqing Gao, et al.. (2023). Precise genome engineering in Pseudomonas using phage-encoded homologous recombination and the Cascade–Cas3 system. Nature Protocols. 18(9). 2642–2670. 17 indexed citations
7.
Medina‐Ruiz, Laura, Robin Bartolini, Gillian Wilson, et al.. (2022). Analysis of combinatorial chemokine receptor expression dynamics using multi-receptor reporter mice. eLife. 11. 15 indexed citations
8.
Li, Yue, Kai Gong, Yingying Yu, et al.. (2022). Biocontrol of strawberry gray mold caused by Botrytis cinerea with the termite associated Streptomyces sp. sdu1201 and actinomycin D. Frontiers in Microbiology. 13. 1051730–1051730. 19 indexed citations
9.
Li, Ruijuan, Qiong Duan, Chaoyi Song, et al.. (2021). Development and application of an efficient recombineering system for Burkholderia glumae and Burkholderia plantarii. Microbial Biotechnology. 14(4). 1809–1826. 23 indexed citations
10.
Li, Aiying, Yang Liu, Xiaoju Li, et al.. (2021). Genome-Guided Discovery of Highly Oxygenated Aromatic Polyketides, Saccharothrixins D–M, from the Rare Marine Actinomycete Saccharothrix sp. D09. Journal of Natural Products. 84(11). 2875–2884. 17 indexed citations
11.
Zhang, Qinyu, Christian Much, Ronald Naumann, et al.. (2020). MLL4 is required after implantation whereas MLL3 becomes essential during late gestation. Development. 147(12). 21 indexed citations
12.
Jing, Xiaoshu, Jia Yin, Vinothkannan Ravichandran, et al.. (2018). Engineering Pseudomonas protegens Pf‐5 to improve its antifungal activity and nitrogen fixation. Microbial Biotechnology. 13(1). 118–133. 42 indexed citations
13.
Tu, Qiang, Jia Yin, Jun Fu, et al.. (2016). Room temperature electrocompetent bacterial cells improve DNA transformation and recombineering efficiency. Scientific Reports. 6(1). 65 indexed citations
14.
Wang, Hailong, Zhen Li, Yu Hou, et al.. (2016). RecET direct cloning and Redαβ recombineering of biosynthetic gene clusters, large operons or single genes for heterologous expression. Nature Protocols. 11(7). 1175–1190. 139 indexed citations
15.
Bian, Xiaoying, Liqiu Xia, Xuezhi Ding, et al.. (2013). Improved seamless mutagenesis by recombineering using ccdB for counterselection. Nucleic Acids Research. 42(5). e37–e37. 109 indexed citations
16.
Fu, Jun, et al.. (2012). A polymorphism in the 5'-flanking region of the melanocortin-4 receptor gene is associated with carcass traits in quails. The Indian Journal of Animal Sciences. 82(3). 229–38. 1 indexed citations
17.
Koul, Dimpy, Jun Fu, Ruijun Shen, et al.. (2011). Antitumor Activity of NVP-BKM120—A Selective Pan Class I PI3 Kinase Inhibitor Showed Differential Forms of Cell Death Based on p53 Status of Glioma Cells. Clinical Cancer Research. 18(1). 184–195. 132 indexed citations
18.
Fu, Jun, et al.. (2010). A Recombineering Pipeline to Make Conditional Targeting Constructs. Methods in enzymology on CD-ROM/Methods in enzymology. 477. 125–144. 70 indexed citations
19.
Maresca, Marcello, Axel Erler, Jun Fu, et al.. (2010). Single-stranded heteroduplex intermediates in λ Red homologous recombination. BMC Molecular Biology. 11(1). 54–54. 97 indexed citations
20.
Fu, Jun, et al.. (2008). Activities of DNA-PK and Ku86, but not Ku70, may predict sensitivity to cisplatin in human gliomas. Journal of Neuro-Oncology. 89(1). 27–35. 18 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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