Su Yang

3.8k total citations
93 papers, 2.4k citations indexed

About

Su Yang is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience and Oncology. According to data from OpenAlex, Su Yang has authored 93 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 47 papers in Molecular Biology, 21 papers in Cellular and Molecular Neuroscience and 21 papers in Oncology. Recurrent topics in Su Yang's work include Genetic Neurodegenerative Diseases (18 papers), Mitochondrial Function and Pathology (15 papers) and CAR-T cell therapy research (14 papers). Su Yang is often cited by papers focused on Genetic Neurodegenerative Diseases (18 papers), Mitochondrial Function and Pathology (15 papers) and CAR-T cell therapy research (14 papers). Su Yang collaborates with scholars based in China, United States and United Kingdom. Su Yang's co-authors include Xiao‐Jiang Li, Shihua Li, Shanshan Huang, Huiming Yang, Bin Liu, Scott Bidlingmaier, Shihua Li, Beisha Tang, Jifeng Guo and Zhaohui Qin and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Clinical Investigation and Nature Communications.

In The Last Decade

Su Yang

87 papers receiving 2.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Su Yang China 26 1.4k 550 510 299 290 93 2.4k
Vittorio de Franciscis Italy 36 2.9k 2.0× 437 0.8× 304 0.6× 253 0.8× 274 0.9× 99 3.9k
Chieko Koike Japan 23 2.2k 1.5× 393 0.7× 732 1.4× 174 0.6× 183 0.6× 48 3.0k
Shuli Xia United States 29 1.7k 1.2× 496 0.9× 197 0.4× 239 0.8× 166 0.6× 66 2.6k
Denise M. Gibo United States 23 855 0.6× 629 1.1× 503 1.0× 641 2.1× 241 0.8× 34 1.9k
Jong Wook Chang South Korea 31 1.4k 1.0× 273 0.5× 330 0.6× 166 0.6× 133 0.5× 78 2.8k
Christoph P. Beier Denmark 30 1.6k 1.1× 1.4k 2.6× 371 0.7× 401 1.3× 152 0.5× 82 3.9k
Lin Cheng China 25 1.9k 1.3× 990 1.8× 242 0.5× 465 1.6× 183 0.6× 75 3.4k
Dara Kallop United States 13 1.2k 0.8× 579 1.1× 278 0.5× 427 1.4× 150 0.5× 13 2.1k
James R. Tonra United States 29 1.2k 0.8× 968 1.8× 436 0.9× 491 1.6× 80 0.3× 70 2.9k
Jere E. Meredith United States 17 1.4k 0.9× 360 0.7× 217 0.4× 319 1.1× 157 0.5× 27 2.8k

Countries citing papers authored by Su Yang

Since Specialization
Citations

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

Fields of papers citing papers by Su Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Su Yang

This figure shows the co-authorship network connecting the top 25 collaborators of Su Yang. A scholar is included among the top collaborators of Su Yang 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 Su Yang. Su Yang 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.
Yang, Su, et al.. (2025). Enhancement of CAR-T cell efficacy and persistence via CD30 costimulation and NF-κB signaling. Molecular Cancer. 24(1). 289–289.
2.
3.
Yang, Bicheng, et al.. (2025). Advances in the combination of CAR-T therapy with small-molecule reagents for hematologic malignancies. Frontiers in Immunology. 16. 1663522–1663522. 1 indexed citations
4.
5.
Wang, Yuepeng, et al.. (2023). The association of growth differentiation factor 5 rs143383 gene polymorphism with osteoarthritis: a systematic review and meta-analysis. Journal of Orthopaedic Surgery and Research. 18(1). 763–763. 2 indexed citations
6.
Ying, Zhitao, Haiyan Yang, Ye Guo, et al.. (2023). Long-term outcomes of relmacabtagene autoleucel in Chinese patients with relapsed/refractory large B-cell lymphoma: Updated results of the RELIANCE study. Cytotherapy. 25(5). 521–529. 13 indexed citations
7.
Hart, Jonathan R., Xiao Liu, Lynn Ueno, et al.. (2022). Nanobodies and chemical cross-links advance the structural and functional analysis of PI3Kα. Proceedings of the National Academy of Sciences. 119(38). e2210769119–e2210769119. 11 indexed citations
8.
Favalli, Nicholas, Gabriele Bassi, Christian Pellegrino, et al.. (2021). Publisher Correction: Stereo- and regiodefined DNA-encoded chemical libraries enable efficient tumour-targeting applications. Nature Chemistry. 13(7). 714–714. 1 indexed citations
9.
Favalli, Nicholas, Gabriele Bassi, Christian Pellegrino, et al.. (2021). Stereo- and regiodefined DNA-encoded chemical libraries enable efficient tumour-targeting applications. Nature Chemistry. 13(6). 540–548. 54 indexed citations
10.
Hyrenius‐Wittsten, Axel, et al.. (2021). SynNotch CAR circuits enhance solid tumor recognition and promote persistent antitumor activity in mouse models. Science Translational Medicine. 13(591). 174 indexed citations
11.
Yang, Su, et al.. (2020). ALPPL2 Is a Highly Specific and Targetable Tumor Cell Surface Antigen. Cancer Research. 80(20). 4552–4564. 22 indexed citations
12.
Sherbenou, Daniel W., Su Yang, Christopher R. Behrens, et al.. (2020). Potent Activity of an Anti-ICAM1 Antibody–Drug Conjugate against Multiple Myeloma. Clinical Cancer Research. 26(22). 6028–6038. 18 indexed citations
13.
Yang, Su, Huiming Yang, Luoxiu Huang, et al.. (2020). Lack of RAN-mediated toxicity in Huntington’s disease knock-in mice. Proceedings of the National Academy of Sciences. 117(8). 4411–4417. 27 indexed citations
14.
Yang, Huiming, Su Yang, Jing Liang, et al.. (2020). Truncation of mutant huntingtin in knock-in mice demonstrates exon1 huntingtin is a key pathogenic form. Nature Communications. 11(1). 2582–2582. 55 indexed citations
15.
Yang, Su, et al.. (2019). Manipulation of Cell-Type Selective Antibody Internalization by a Guide-Effector Bispecific Design. Molecular Cancer Therapeutics. 18(6). 1092–1103. 21 indexed citations
16.
Li, Li, Wen Wang, Shu Cheng, et al.. (2019). Clinical Efficacy and Tumor Microenvironment Influence in a Dose-Escalation Study of Anti-CD19 Chimeric Antigen Receptor T Cells in Refractory B-Cell Non-Hodgkin's Lymphoma. Clinical Cancer Research. 25(23). 6995–7003. 88 indexed citations
17.
Ying, Zhitao, Pengpeng Xu, Li Wang, et al.. (2019). Clinical Response in Relapsed/Refractory (R/R) B-NHL Treated with the CD19-Directed CAR T-Cell Product JWCAR029. Blood. 134(Supplement_1). 2876–2876. 7 indexed citations
18.
Yan, Zi‐Xun, Wen Wang, Zhong Zheng, et al.. (2018). Efficacy and Safety of JWCAR029 in Adult Patients with Relapsed and Refractory B-Cell Non-Hodgkin Lymphoma. Blood. 132(Supplement 1). 4187–4187. 6 indexed citations
19.
Yang, Su, Renbao Chang, Huiming Yang, et al.. (2017). CRISPR/Cas9-mediated gene editing ameliorates neurotoxicity in mouse model of Huntington’s disease. Journal of Clinical Investigation. 127(7). 2719–2724. 277 indexed citations
20.
Helbich, Thomas H., Timothy P. L. Roberts, Axel Goßmann, et al.. (2000). Quantitative gadopentetate-enhanced MRI of breast tumors: Testing of different analytic methods. Magnetic Resonance in Medicine. 44(6). 915–924. 38 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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