Yu Tang

4.3k total citations · 1 hit paper
47 papers, 3.3k citations indexed

About

Yu Tang is a scholar working on Molecular Biology, Neurology and Cellular and Molecular Neuroscience. According to data from OpenAlex, Yu Tang has authored 47 papers receiving a total of 3.3k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Molecular Biology, 13 papers in Neurology and 7 papers in Cellular and Molecular Neuroscience. Recurrent topics in Yu Tang's work include CRISPR and Genetic Engineering (13 papers), Pluripotent Stem Cells Research (10 papers) and Parkinson's Disease Mechanisms and Treatments (6 papers). Yu Tang is often cited by papers focused on CRISPR and Genetic Engineering (13 papers), Pluripotent Stem Cells Research (10 papers) and Parkinson's Disease Mechanisms and Treatments (6 papers). Yu Tang collaborates with scholars based in China, United States and Hong Kong. Yu Tang's co-authors include Weidong Le, Juan Yang, Ting Li, Jia Li, Dehua Yang, Chun‐Li Zhang, Meng-Lu Liu, Tong Zang, Xiaojie Zhang and Sheng Chen and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Journal of Biological Chemistry.

In The Last Decade

Yu Tang

46 papers receiving 3.3k citations

Hit Papers

Differential Roles of M1 and M2 Microglia in Neurodegener... 2015 2026 2018 2022 2015 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
Yu Tang China 22 1.4k 1.3k 738 535 523 47 3.3k
Fabián Docagne France 41 1.2k 0.9× 1.4k 1.1× 564 0.8× 1.2k 2.2× 526 1.0× 74 4.7k
Jian Zou China 30 1.4k 1.0× 1.2k 0.9× 381 0.5× 701 1.3× 261 0.5× 75 3.8k
Tim Magnus Germany 27 1.3k 0.9× 1.1k 0.8× 623 0.8× 772 1.4× 458 0.9× 41 3.8k
Mithilesh Kumar Jha South Korea 29 880 0.6× 1.0k 0.8× 616 0.8× 576 1.1× 338 0.6× 54 2.9k
Thomas Koeglsperger Germany 15 1.8k 1.3× 845 0.6× 981 1.3× 817 1.5× 623 1.2× 30 3.5k
Melissa K. McCoy United States 15 958 0.7× 1.0k 0.8× 483 0.7× 731 1.4× 852 1.6× 22 2.7k
Yoshifumi Sonobe Japan 30 1.3k 1.0× 1.1k 0.8× 643 0.9× 520 1.0× 330 0.6× 44 3.4k
Renzo Mancuso Spain 25 1.4k 1.0× 1.1k 0.8× 956 1.3× 537 1.0× 651 1.2× 50 3.0k
Jun Kawanokuchi Japan 34 2.0k 1.4× 1.2k 0.9× 791 1.1× 837 1.6× 404 0.8× 51 4.4k
Alain R. Simard Canada 23 2.0k 1.5× 1.2k 0.9× 912 1.2× 507 0.9× 222 0.4× 35 3.6k

Countries citing papers authored by Yu Tang

Since Specialization
Citations

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

Fields of papers citing papers by Yu Tang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yu Tang

This figure shows the co-authorship network connecting the top 25 collaborators of Yu Tang. A scholar is included among the top collaborators of Yu 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 Yu Tang. Yu 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.
Li, Xiao, Yu Tang, Lei Zhang, et al.. (2025). Clinical features of intracardiac thrombotic complication in patients with severe Mycoplasma pneumoniae pneumonia. ˜The œItalian Journal of Pediatrics/Italian journal of pediatrics. 51(1). 42–42. 1 indexed citations
3.
Zhang, Mengfei, et al.. (2022). The efficient generation of knockout microglia cells using a dual-sgRNA strategy by CRISPR/Cas9. Frontiers in Molecular Neuroscience. 15. 1008827–1008827. 6 indexed citations
4.
Li, Chaoyi, Jie Ren, Mengfei Zhang, et al.. (2022). The heterogeneity of microglial activation and its epigenetic and non-coding RNA regulations in the immunopathogenesis of neurodegenerative diseases. Cellular and Molecular Life Sciences. 79(10). 511–511. 29 indexed citations
5.
Li, Chaoyi, Qian Chen, Jie Ren, et al.. (2022). Identification and characterization of two novel noncoding tyrosinase (TYR) gene variants leading to oculocutaneous albinism type 1. Journal of Biological Chemistry. 298(5). 101922–101922. 6 indexed citations
6.
Ding, Baojin, Yu Tang, Shuaipeng Ma, et al.. (2021). Disease Modeling with Human Neurons Reveals LMNB1 Dysregulation Underlying DYT1 Dystonia. Journal of Neuroscience. 41(9). 2024–2038. 27 indexed citations
7.
Tang, Yu, Jie Ren, & Chuanchang Li. (2021). Establishment of a GFP::LMNB1 knockin cell line (CSUi002-A-1) from a dystonia patient-specific iPSC by CRISPR/Cas9 editing. Stem Cell Research. 55. 102505–102505. 2 indexed citations
8.
Wang, Lijing, et al.. (2021). An induced pluripotent stem cell line (CSUi004-A) from skin fibroblasts of a healthy individual. Stem Cell Research. 53. 102336–102336. 3 indexed citations
9.
Zhao, Guihu, Jinchen Li, & Yu Tang. (2020). AsCRISPR: A Web Server for Allele-Specific Single Guide RNA Design in Precision Medicine. The CRISPR Journal. 3(6). 512–522. 11 indexed citations
10.
Zhang, Wenhui, Tomomi Aida, Ricardo C.H. del Rosario, et al.. (2020). Multiplex precise base editing in cynomolgus monkeys. Nature Communications. 11(1). 2325–2325. 29 indexed citations
11.
Tang, Yu, Meng-Lu Liu, Tong Zang, & Chun‐Li Zhang. (2017). Direct Reprogramming Rather than iPSC-Based Reprogramming Maintains Aging Hallmarks in Human Motor Neurons. Frontiers in Molecular Neuroscience. 10. 359–359. 110 indexed citations
12.
Lin, Chengzhong, Wei Lu, Zhenhu Ren, et al.. (2016). Elevated RET expression enhances EGFR activation and mediates EGFR inhibitor resistance in head and neck squamous cell carcinoma. Cancer Letters. 377(1). 1–10. 17 indexed citations
13.
Tang, Yu & Weidong Le. (2015). Differential Roles of M1 and M2 Microglia in Neurodegenerative Diseases. Molecular Neurobiology. 53(2). 1181–1194. 1695 indexed citations breakdown →
14.
Li, Ting, Dehua Yang, Jia Li, et al.. (2014). Critical Role of Tet3 in Neural Progenitor Cell Maintenance and Terminal Differentiation. Molecular Neurobiology. 51(1). 142–154. 66 indexed citations
15.
Tang, Yu, et al.. (2013). Adaptive changes in autophagy after UPS impairment in Parkinson's disease. Acta Pharmacologica Sinica. 34(5). 667–673. 46 indexed citations
16.
Jia, Li, Ting Li, Xiaojie Zhang, et al.. (2013). Human superoxide dismutase 1 overexpression in motor neurons of Caenorhabditis elegans causes axon guidance defect and neurodegeneration. Neurobiology of Aging. 35(4). 837–846. 21 indexed citations
17.
Tang, Yu, Tengda Li, Junyi Li, et al.. (2013). Jmjd3 is essential for the epigenetic modulation of microglia phenotypes in the immune pathogenesis of Parkinson’s disease. Cell Death and Differentiation. 21(3). 369–380. 175 indexed citations
18.
19.
Li, Lixi, Sufang Zhang, Xin Zhang, et al.. (2013). Autophagy Enhancer Carbamazepine Alleviates Memory Deficits and Cerebral Amyloid-β Pathology in a Mouse Model of Alzheimer's Disease. Current Alzheimer Research. 10(4). 433–441. 128 indexed citations
20.
Yang, Dehua, Ting Li, Yi Wang, et al.. (2012). miR-132 regulates the differentiation of dopamine neurons by directly targeting Nurr1 expression. Journal of Cell Science. 125(Pt 7). 1673–82. 138 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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