Ruifan Wu

2.0k total citations · 1 hit paper
36 papers, 1.5k citations indexed

About

Ruifan Wu is a scholar working on Molecular Biology, Cancer Research and Physiology. According to data from OpenAlex, Ruifan Wu has authored 36 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Molecular Biology, 9 papers in Cancer Research and 8 papers in Physiology. Recurrent topics in Ruifan Wu's work include RNA modifications and cancer (19 papers), Cancer-related gene regulation (14 papers) and Cancer-related molecular mechanisms research (8 papers). Ruifan Wu is often cited by papers focused on RNA modifications and cancer (19 papers), Cancer-related gene regulation (14 papers) and Cancer-related molecular mechanisms research (8 papers). Ruifan Wu collaborates with scholars based in China, Indonesia and United States. Ruifan Wu's co-authors include Xinxia Wang, Yizhen Wang, Yongxi Yao, Yuanling Zhao, Zhen Bi, Fengqin Wang, Youhua Liu, Youhua Liu, Min Cai and Qin Jiang and has published in prestigious journals such as Nature Communications, Journal of Agricultural and Food Chemistry and Journal of Computational Physics.

In The Last Decade

Ruifan Wu

30 papers receiving 1.5k citations

Hit Papers

m6A mRNA methylation controls autophagy and adipogenesis ... 2019 2026 2021 2023 2019 50 100 150 200 250
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. m6A mRNA methylation controls autophagy and adipogenesis by targeting Atg5 and Atg7
    2019 297 citations Xinxia Wang, Ruifan Wu et al. Autophagy profile →

Peers

Ruifan Wu
Huiwen Song China
Guoqiang Wang China
Feizhou Huang China
Irina Tikhanovich United States
Xiaoying Lou China
D P Chauhan United States
Ming Su China
Francesca Conserva Italy
Saiful A. Mir United States
Huiwen Song China
Ruifan Wu
Citations per year, relative to Ruifan Wu Ruifan Wu (= 1×) peers Huiwen Song

Countries citing papers authored by Ruifan Wu

Since Specialization
Citations

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

Fields of papers citing papers by Ruifan Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ruifan Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Ruifan Wu. A scholar is included among the top collaborators of Ruifan 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 Ruifan Wu. Ruifan 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.
Song, Xiaofeng, et al.. (2025). Gut microbiota and metabolites in lipid metabolism and intramuscular fat deposition: mechanisms and implications for meat quality. Journal of Animal Science and Biotechnology. 16(1). 147–147.
2.
Pan, L. G., Qian Zhou, Andrew I. Su, et al.. (2025). The gut-brain axis mechanism of normal appetite induced by kynurenic acid. Cell Reports. 44(5). 115659–115659. 4 indexed citations
3.
4.
Fu, Yiming, Fenglin Zhang, Shuyi Liang, et al.. (2025). Inhibition of DHHC9-mediated CD36 palmitoylation lessens high-fat diet (HFD)-induced impairment of pubertal mammary gland development through the JNK-ERK pathway. Cellular & Molecular Biology Letters. 30(1). 117–117.
5.
Liang, Shuyi, Yiming Fu, Junfeng Wang, et al.. (2024). Knockdown of the VEGFB/VEGFR1 signaling suppresses pubertal mammary gland development of mice via the inhibition of PI3K/Akt pathway. International Journal of Biological Macromolecules. 264(Pt 2). 130782–130782. 1 indexed citations
6.
Yuan, Yexian, Shaolei Xiong, Zilai Wang, et al.. (2024). Macrophage-derived chemokine CCL22 establishes local LN-mediated adaptive thermogenesis and energy expenditure. Science Advances. 10(26). eadn5229–eadn5229. 4 indexed citations
7.
Wu, Ruifan, Zhipei Chen, Lai Peng, et al.. (2024). A graph-learning based model for automatic diagnosis of Sjögren’s syndrome on digital pathological images: a multicentre cohort study. Journal of Translational Medicine. 22(1). 748–748. 3 indexed citations
8.
Liu, Jinhao, Junfeng Wang, Lin Cai, et al.. (2024). Knockdown of VEGF-B improves HFD-induced insulin resistance by enhancing glucose uptake in vascular endothelial cells via the PI3K/Akt pathway. International Journal of Biological Macromolecules. 285. 138279–138279. 2 indexed citations
9.
10.
Liu, Yanzhi, Ruifan Wu, & Ying Jiang. (2024). Binary structured physics-informed neural networks for solving equations with rapidly changing solutions. Journal of Computational Physics. 518. 113341–113341. 5 indexed citations
11.
Wu, Ruifan, Yexian Yuan, Shaolei Xiong, et al.. (2023). Genetically prolonged beige fat in male mice confers long-lasting metabolic health. Nature Communications. 14(1). 2731–2731. 15 indexed citations
12.
Wu, Ruifan, Fan Li, Gang Shu, et al.. (2023). Transcriptional and post-transcriptional control of autophagy and adipogenesis by YBX1. Cell Death and Disease. 14(1). 29–29. 24 indexed citations
13.
Chen, Wei, Yushi Chen, Ruifan Wu, et al.. (2022). DHA alleviates diet-induced skeletal muscle fiber remodeling via FTO/m6A/DDIT4/PGC1α signaling. BMC Biology. 20(1). 39–39. 30 indexed citations
14.
Zeng, Botao, Ruifan Wu, Yushi Chen, et al.. (2022). FTO knockout in adipose tissue effectively alleviates hepatic steatosis partially via increasing the secretion of adipocyte-derived IL-6. Gene. 818. 146224–146224. 6 indexed citations
15.
Chen, Yushi, Ruifan Wu, Wei Chen, et al.. (2021). Curcumin prevents obesity by targeting TRAF4‐induced ubiquitylation in m 6 A‐dependent manner. EMBO Reports. 22(5). e52146–e52146. 69 indexed citations
16.
Wang, Xinxia, Ruifan Wu, Youhua Liu, et al.. (2019). m6A mRNA methylation controls autophagy and adipogenesis by targeting Atg5 and Atg7. Autophagy. 16(7). 1221–1235. 297 indexed citations breakdown →
17.
Wu, Ruifan, Guanqun Guo, Zhen Bi, et al.. (2019). m6A methylation modulates adipogenesis through JAK2-STAT3-C/EBPβ signaling. Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms. 1862(8). 796–806. 51 indexed citations
18.
Wu, Ruifan, et al.. (2019). Understanding m6A Function Through Uncovering the Diversity Roles of YTH Domain-Containing Proteins. Molecular Biotechnology. 61(5). 355–364. 28 indexed citations
19.
Wang, Xinxia, Baofa Sun, Qin Jiang, et al.. (2018). mRNA m6A plays opposite role in regulating UCP2 and PNPLA2 protein expression in adipocytes. International Journal of Obesity. 42(11). 1912–1924. 43 indexed citations
20.
Wu, Ruifan, et al.. (2016). N 6-Methyladenosine (m6A) Methylation in mRNA with A Dynamic and Reversible Epigenetic Modification. Molecular Biotechnology. 58(7). 450–459. 110 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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