Hong-Wen Tang

1.5k total citations · 1 hit paper
26 papers, 1.1k citations indexed

About

Hong-Wen Tang is a scholar working on Molecular Biology, Epidemiology and Cell Biology. According to data from OpenAlex, Hong-Wen Tang has authored 26 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Molecular Biology, 13 papers in Epidemiology and 7 papers in Cell Biology. Recurrent topics in Hong-Wen Tang's work include Autophagy in Disease and Therapy (13 papers), Muscle Physiology and Disorders (5 papers) and Endoplasmic Reticulum Stress and Disease (4 papers). Hong-Wen Tang is often cited by papers focused on Autophagy in Disease and Therapy (13 papers), Muscle Physiology and Disorders (5 papers) and Endoplasmic Reticulum Stress and Disease (4 papers). Hong-Wen Tang collaborates with scholars based in United States, Singapore and Taiwan. Hong-Wen Tang's co-authors include Norbert Perrimon, Guang‐Chao Chen, Shu‐Yu Lin, Yanhui Hu, Mei-Hsuan Wu, Yubao Wang, Hong‐Ru Lin, Jing Li, Jian Guo and Yan Dong and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Nature Communications.

In The Last Decade

Hong-Wen Tang

25 papers receiving 1.1k citations

Hit Papers

Mitochondrial transfer mediates endothelial cell engraftm... 2024 2026 2025 2024 25 50 75 100

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Hong-Wen Tang United States 18 702 345 171 149 131 26 1.1k
Hanneke Okkenhaug United Kingdom 18 698 1.0× 268 0.8× 255 1.5× 99 0.7× 124 0.9× 30 1.1k
Ruhee Dere United States 16 1.1k 1.5× 325 0.9× 222 1.3× 192 1.3× 70 0.5× 27 1.4k
Oishee Chakrabarti India 22 885 1.3× 285 0.8× 325 1.9× 92 0.6× 111 0.8× 46 1.3k
Kanji Okumoto Japan 27 1.8k 2.6× 170 0.5× 104 0.6× 170 1.1× 178 1.4× 46 1.9k
Mariana E. G. de Araújo Austria 14 858 1.2× 359 1.0× 500 2.9× 92 0.6× 108 0.8× 21 1.4k
Jukka Kallijärvi Finland 19 753 1.1× 96 0.3× 95 0.6× 134 0.9× 161 1.2× 41 1.1k
Jiashun Zheng United States 16 1.3k 1.9× 107 0.3× 165 1.0× 96 0.6× 68 0.5× 23 1.6k
Lawrence D. Schweitzer United States 9 920 1.3× 200 0.6× 309 1.8× 160 1.1× 190 1.5× 10 1.3k
Moonsun Hwang United States 9 881 1.3× 97 0.3× 188 1.1× 50 0.3× 123 0.9× 11 1.2k
Annett Koch Germany 17 1.4k 2.0× 238 0.7× 401 2.3× 82 0.6× 401 3.1× 17 1.7k

Countries citing papers authored by Hong-Wen Tang

Since Specialization
Citations

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

Fields of papers citing papers by Hong-Wen Tang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hong-Wen Tang. 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 Hong-Wen Tang. The network helps show where Hong-Wen Tang may publish in the future.

Co-authorship network of co-authors of Hong-Wen Tang

This figure shows the co-authorship network connecting the top 25 collaborators of Hong-Wen Tang. A scholar is included among the top collaborators of Hong-Wen Tang 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 Hong-Wen Tang. Hong-Wen Tang 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.
Lim, Zhi Wei, et al.. (2025). Transcriptional regulation of autophagy in skeletal muscle stem cells. Disease Models & Mechanisms. 18(2). 4 indexed citations
2.
Gou, Qian, et al.. (2025). Exercise suppresses DEAF1 to normalize mTORC1 activity and reverse muscle aging. Proceedings of the National Academy of Sciences. 122(48). e2508893122–e2508893122.
3.
Im, Gwang‐Bum, Yonglin Zhu, Xuechong Hong, et al.. (2024). Mitochondrial transfer mediates endothelial cell engraftment through mitophagy. Nature. 629(8012). 660–668. 106 indexed citations breakdown →
4.
Chin, Hui San, Shu‐Yi Huang, Nai Yang Fu, et al.. (2024). FOXO-regulated DEAF1 controls muscle regeneration through autophagy. Autophagy. 20(12). 2632–2654. 10 indexed citations
5.
Xu, Chiwei, Jun Xu, Hong-Wen Tang, et al.. (2023). A phosphate-sensing organelle regulates phosphate and tissue homeostasis. Nature. 617(7962). 798–806. 25 indexed citations
6.
Tang, Hong-Wen, et al.. (2022). Terahertz metamaterial biosensor for diagnosis of hepatocellular carcinoma at early stage. Applied Optics. 61(16). 4817–4817. 8 indexed citations
7.
Wang, Yuanli, et al.. (2022). A Novel TP53 Gene Mutation Sustains Non-Small Cell Lung Cancer through Mitophagy. Cells. 11(22). 3587–3587. 12 indexed citations
8.
Tang, Hong-Wen, Jui–Hsia Weng, Yanhui Hu, et al.. (2021). mTORC1-chaperonin CCT signaling regulates m 6 A RNA methylation to suppress autophagy. Proceedings of the National Academy of Sciences. 118(10). 53 indexed citations
9.
Cho, Sungyun, Gina Lee, Brian F. Pickering, et al.. (2021). mTORC1 promotes cell growth via m6A-dependent mRNA degradation. Molecular Cell. 81(10). 2064–2075.e8. 71 indexed citations
10.
Wang, Wenjun, Jianshuang Li, Miaomiao Wang, et al.. (2021). Endonuclease G promotes autophagy by suppressing mTOR signaling and activating the DNA damage response. Nature Communications. 12(1). 476–476. 52 indexed citations
11.
Tang, Hong-Wen, Virender Singh, Ashwani Kumar Thakur, et al.. (2020). A Drosophila model of oral peptide therapeutics for adult intestinal stem cell tumors. Disease Models & Mechanisms. 13(7). 7 indexed citations
12.
Gu, Lei, Longfei Wang, Hao Chen, et al.. (2020). CG14906 (mettl4) mediates m6A methylation of U2 snRNA in Drosophila. Cell Discovery. 6(1). 44–44. 38 indexed citations
13.
Song, Wei, Serkan Kır, Shangyu Hong, et al.. (2019). Tumor-Derived Ligands Trigger Tumor Growth and Host Wasting via Differential MEK Activation. Developmental Cell. 48(2). 277–286.e6. 57 indexed citations
14.
Xu, Chiwei, Hong-Wen Tang, Yanhui Hu, et al.. (2019). An in vivo RNAi screen uncovers the role of AdoR signaling and adenosine deaminase in controlling intestinal stem cell activity. Proceedings of the National Academy of Sciences. 117(1). 464–471. 17 indexed citations
15.
Tang, Hong-Wen, Yanhui Hu, Chiao‐Lin Chen, et al.. (2018). The TORC1-Regulated CPA Complex Rewires an RNA Processing Network to Drive Autophagy and Metabolic Reprogramming. Cell Metabolism. 27(5). 1040–1054.e8. 62 indexed citations
16.
Guo, Jian, Hong-Wen Tang, Jing Li, Norbert Perrimon, & Yan Dong. (2018). Xio is a component of the Drosophila sex determination pathway and RNA N 6 -methyladenosine methyltransferase complex. Proceedings of the National Academy of Sciences. 115(14). 3674–3679. 76 indexed citations
18.
Kang, Ming-Lun, Yi‐Chun Chen, Hong-Wen Tang, et al.. (2012). Autophagy-related gene 7 is downstream of heat shock protein 27 in the regulation of eye morphology, polyglutamine toxicity, and lifespan in Drosophila. Journal of Biomedical Science. 19(1). 52–52. 36 indexed citations
19.
Tang, Hong-Wen & Guang‐Chao Chen. (2011). Unraveling the role of myosin in forming autophagosomes. Autophagy. 7(7). 778–779. 10 indexed citations
20.
Chen, Guang‐Chao, Janice Y. Lee, Hong-Wen Tang, et al.. (2008). Genetic interactions betweenDrosophila melanogasterAtg1 and paxillin reveal a role for paxillin in autophagosome formation. Autophagy. 4(1). 37–45. 51 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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