Qingfa Wu

9.9k total citations · 2 hit papers
76 papers, 5.6k citations indexed

About

Qingfa Wu is a scholar working on Molecular Biology, Plant Science and Endocrinology. According to data from OpenAlex, Qingfa Wu has authored 76 papers receiving a total of 5.6k indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Molecular Biology, 31 papers in Plant Science and 17 papers in Endocrinology. Recurrent topics in Qingfa Wu's work include Plant Virus Research Studies (27 papers), Plant and Fungal Interactions Research (17 papers) and Insect symbiosis and bacterial influences (8 papers). Qingfa Wu is often cited by papers focused on Plant Virus Research Studies (27 papers), Plant and Fungal Interactions Research (17 papers) and Insect symbiosis and bacterial influences (8 papers). Qingfa Wu collaborates with scholars based in China, United States and France. Qingfa Wu's co-authors include Shou‐Wei Ding, Zhaoxia Chang, Ge Shan, Huijie Liu, Pengfei Zhu, Liang Chen, Ya Jia, Zhaoyong Li, Xiaolin Wang and Mei Lin and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Nature Communications.

In The Last Decade

Qingfa Wu

71 papers receiving 5.5k citations

Hit Papers

Exon-intron circular RNAs regulate transcription in the n... 2010 2026 2015 2020 2015 2010 500 1000 1.5k 2.0k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Qingfa Wu China 28 3.7k 2.5k 1.9k 791 678 76 5.6k
Kenji Nakahara Japan 29 2.1k 0.6× 982 0.4× 1.3k 0.7× 348 0.4× 366 0.5× 81 3.9k
Megha Ghildiyal United States 9 2.6k 0.7× 1.2k 0.5× 1.1k 0.6× 139 0.2× 129 0.2× 9 3.3k
Amy H. Buck United Kingdom 31 2.5k 0.7× 1.1k 0.4× 555 0.3× 108 0.1× 364 0.5× 67 4.2k
Michael P. Terns United States 49 7.7k 2.0× 484 0.2× 517 0.3× 357 0.5× 645 1.0× 99 8.2k
Susan Parrish United States 10 3.6k 1.0× 1.1k 0.4× 880 0.5× 83 0.1× 112 0.2× 13 4.5k
Rebecca M. Terns United States 43 6.6k 1.8× 460 0.2× 476 0.3× 316 0.4× 539 0.8× 64 7.2k
Sara Olson United States 32 3.7k 1.0× 866 0.3× 400 0.2× 137 0.2× 223 0.3× 50 6.0k
Patrick Essletzbichler Austria 9 7.7k 2.1× 314 0.1× 1.0k 0.6× 145 0.2× 687 1.0× 11 8.4k
Jeffrey Wilusz United States 49 5.7k 1.5× 567 0.2× 596 0.3× 138 0.2× 787 1.2× 113 7.8k
Mohamed‐Ali Hakimi France 42 3.1k 0.8× 262 0.1× 1.0k 0.6× 123 0.2× 119 0.2× 82 5.8k

Countries citing papers authored by Qingfa Wu

Since Specialization
Citations

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

Fields of papers citing papers by Qingfa Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Qingfa Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Qingfa Wu. A scholar is included among the top collaborators of Qingfa Wu 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 Qingfa Wu. Qingfa Wu 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.
Zhu, Yu, et al.. (2025). In-depth analysis of 17,115 rice transcriptomes reveals extensive viral diversity in rice plants. Nature Communications. 16(1). 1559–1559. 3 indexed citations
2.
Dai, Yuting, et al.. (2025). Characterization of the phenotype and function of PRELP+ fibroblast subtype in liver metastatic colorectal cancer. Frontiers in Genetics. 16. 1615259–1615259.
3.
Ain, Qurat Ul, Xuan Wu, Qing Bai, et al.. (2024). Cyanophage-encoded auxiliary metabolic genes in modulating cyanobacterial metabolism and algal bloom dynamics. SHILAP Revista de lepidopterología. 4. 5 indexed citations
4.
5.
Yan, Wenkai, Wen‐Cheng Liu, Yanhong Han, et al.. (2023). Discovery of aphid-transmitted Rice tiller inhibition virus from native plants through metagenomic sequencing. PLoS Pathogens. 19(3). e1011238–e1011238. 8 indexed citations
6.
Yang, Feng, Qingfa Wu, Yuxing Chen, et al.. (2023). Phylogenomics of five Pseudanabaena cyanophages and evolutionary traces of horizontal gene transfer. Environmental Microbiome. 18(1). 3–3. 6 indexed citations
7.
Ding, Shou‐Wei, et al.. (2021). Culture-Independent Discovery of Viroids by Deep Sequencing and Computational Algorithms. Methods in molecular biology. 2316. 251–274. 1 indexed citations
8.
Zhang, Liqin, Wen Xu, Xinlei Gao, et al.. (2020). lncRNA Sensing of a Viral Suppressor of RNAi Activates Non-canonical Innate Immune Signaling in Drosophila. Cell Host & Microbe. 27(1). 115–128.e8. 51 indexed citations
9.
Yang, Feng, Hua Jin, Xiaoqian Wang, et al.. (2020). Genomic Analysis of Mic1 Reveals a Novel Freshwater Long-Tailed Cyanophage. Frontiers in Microbiology. 11. 484–484. 33 indexed citations
10.
Wang, Zikun, et al.. (2019). Clinicopathological risk factors for gastric cancer: a retrospective cohort study in China. BMJ Open. 9(9). e030639–e030639. 20 indexed citations
11.
Chang, Zhaoxia, Ibukun A. Akinyemi, Dongyang Guo, & Qingfa Wu. (2018). Characterization and comparative analysis of microRNAs in the rice pest Sogatella furcifera. PLoS ONE. 13(9). e0204517–e0204517. 10 indexed citations
12.
Akinyemi, Ibukun A., et al.. (2018). Genome characterization of the newly identified maize-associated totivirus Anhui. Archives of Virology. 163(10). 2929–2931. 8 indexed citations
13.
Chang, Zhaoxia, Nan Tang, Lin Wang, et al.. (2016). Identification and characterization of microRNAs in the white-backed planthopper,Sogatella furcifera. Insect Science. 23(3). 452–468. 25 indexed citations
14.
Yu, Gino, et al.. (2015). Complete genome sequence of a novel velarivirus infecting areca palm in China. Archives of Virology. 160(9). 2367–2370. 16 indexed citations
15.
Liu, Huijie, Xueren Wang, Horng‐Dar Wang, et al.. (2012). Escherichia coli noncoding RNAs can affect gene expression and physiology of Caenorhabditis elegans. Nature Communications. 3(1). 1073–1073. 117 indexed citations
16.
Wu, Qingfa, Yingjun Luo, Rui Lu, et al.. (2010). Virus discovery by deep sequencing and assembly of virus-derived small silencing RNAs. Proceedings of the National Academy of Sciences. 107(4). 1606–1611. 375 indexed citations breakdown →
17.
Wu, Qingfa, Niels Tommerup, San Ming Wang, & Lars Hestbjerg Hansen. (2006). A novel primate specific gene, CEI, is located in the homeobox gene IRXA2 promoter in Homo sapiens. Gene. 371(2). 167–173. 9 indexed citations
18.
Ge, Xijin, et al.. (2005). Annotating nonspecific SAGE tags with microarray data. Genomics. 87(1). 173–180. 7 indexed citations
19.
Wu, Qingfa, Zuyuan Xu, Wei Tian, et al.. (2004). Development of Taqman RT-nested PCR system for clinical SARS-CoV detection. Journal of Virological Methods. 119(1). 17–23. 7 indexed citations
20.
Wu, Qingfa, Yilin Zhang, Hong Lü, et al.. (2003). The E Protein is a Multifunctional Membrane Protein of SARS-CoV. Genomics Proteomics & Bioinformatics. 1(2). 131–144. 30 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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