Yapeng Su

4.4k total citations
29 papers, 933 citations indexed

About

Yapeng Su is a scholar working on Molecular Biology, Immunology and Oncology. According to data from OpenAlex, Yapeng Su has authored 29 papers receiving a total of 933 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Molecular Biology, 11 papers in Immunology and 7 papers in Oncology. Recurrent topics in Yapeng Su's work include Immune Cell Function and Interaction (8 papers), CAR-T cell therapy research (7 papers) and T-cell and B-cell Immunology (6 papers). Yapeng Su is often cited by papers focused on Immune Cell Function and Interaction (8 papers), CAR-T cell therapy research (7 papers) and T-cell and B-cell Immunology (6 papers). Yapeng Su collaborates with scholars based in United States, China and South Africa. Yapeng Su's co-authors include James R. Heath, Wei Wei, Antoni Ribas, Min Xue, Qihui Shi, Jung-Woo Kim, Lu Wei, Jiajun Du, Lídia Robert and Alphonsus H. C. Ng and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Journal of the American Chemical Society.

In The Last Decade

Yapeng Su

26 papers receiving 923 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yapeng Su United States 15 513 228 188 178 137 29 933
Jiani Huang China 16 507 1.0× 178 0.8× 165 0.9× 161 0.9× 37 0.3× 28 1.0k
Sukhwinder Kaur United States 13 620 1.2× 167 0.7× 114 0.6× 250 1.4× 61 0.4× 31 1.0k
Prakash Kshirsagar United States 13 649 1.3× 342 1.5× 115 0.6× 159 0.9× 39 0.3× 28 1.2k
Stephan Rogalla United States 15 265 0.5× 302 1.3× 141 0.8× 110 0.6× 81 0.6× 31 789
Adnan O. Abu‐Yousif United States 15 268 0.5× 487 2.1× 164 0.9× 270 1.5× 48 0.4× 35 1.1k
Rashmi Kshirsagar United States 17 1.1k 2.1× 224 1.0× 89 0.5× 100 0.6× 66 0.5× 26 1.2k
Fabio Gasparri Italy 16 479 0.9× 68 0.3× 45 0.2× 183 1.0× 171 1.2× 37 896
Miriam Dwek United Kingdom 21 849 1.7× 176 0.8× 262 1.4× 176 1.0× 13 0.1× 55 1.3k
Jianbo Pan China 18 534 1.0× 116 0.5× 108 0.6× 89 0.5× 15 0.1× 53 1.3k

Countries citing papers authored by Yapeng Su

Since Specialization
Citations

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

Fields of papers citing papers by Yapeng Su

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yapeng Su

This figure shows the co-authorship network connecting the top 25 collaborators of Yapeng Su. A scholar is included among the top collaborators of Yapeng Su 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 Yapeng Su. Yapeng Su 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.
Su, Yapeng, et al.. (2026). The ubiquitin ligase KLHL6 drives resistance to CD8+ T cell dysfunction. Nature. 651(8105). 451–461. 1 indexed citations
2.
3.
Alencar, Gabriel F., Yapeng Su, Valentin Voillet, et al.. (2025). Triple checkpoint blockade of PD-1, Tim-3, and Lag-3 enhances adoptive T cell immunotherapy in a mouse model of ovarian cancer. Proceedings of the National Academy of Sciences. 122(39). e2419888122–e2419888122.
4.
Chen, Daniel, Jingyi Xie, Jongchan Choi, et al.. (2024). Integrative systems biology reveals NKG2A-biased immune responses correlate with protection in infectious disease, autoimmune disease, and cancer. Cell Reports. 43(3). 113872–113872. 4 indexed citations
5.
Qiu, Yajing, Yapeng Su, Hongcheng Cheng, et al.. (2024). Mannose metabolism reshapes T cell differentiation to enhance anti-tumor immunity. Cancer Cell. 43(1). 103–121.e8. 20 indexed citations
6.
Nag, Arundhati, Amirhossein Mafi, Samir Das, et al.. (2023). Stereochemical engineering yields a multifunctional peptide macrocycle inhibitor of Akt2 by fine-tuning macrocycle-cell membrane interactions. Communications Chemistry. 6(1). 95–95. 2 indexed citations
7.
Xu, Alexander M., William Chour, Yapeng Su, et al.. (2023). Entropic analysis of antigen-specific CDR3 domains identifies essential binding motifs shared by CDR3s with different antigen specificities. Cell Systems. 14(4). 273–284.e5. 3 indexed citations
8.
Chen, Daniel, Jingyi Xie, Yapeng Su, & James R. Heath. (2023). T cell receptor sequences are the dominant factor contributing to the phenotype of CD8+ T cells with specificities against immunogenic viral antigens. Cell Reports. 42(11). 113279–113279. 9 indexed citations
9.
Shemesh, Avishai, Yapeng Su, Daniel R. Calabrese, et al.. (2022). Diminished cell proliferation promotes natural killer cell adaptive-like phenotype by limiting FcεRIγ expression. The Journal of Experimental Medicine. 219(11). 23 indexed citations
10.
Ng, Raymond T., J. Lee, Priyanka Baloni, et al.. (2022). Constraint-Based Reconstruction and Analyses of Metabolic Models: Open-Source Python Tools and Applications to Cancer. Frontiers in Oncology. 12. 914594–914594. 9 indexed citations
11.
Zheng, Hong, Aditya Rao, Denis Đermadi, et al.. (2021). Multi-cohort analysis of host immune response identifies conserved protective and detrimental modules associated with severity across viruses. Immunity. 54(4). 753–768.e5. 34 indexed citations
12.
Du, Jiajun, Yapeng Su, Chenxi Qian, et al.. (2020). Raman-guided subcellular pharmaco-metabolomics for metastatic melanoma cells. Nature Communications. 11(1). 4830–4830. 118 indexed citations
13.
Su, Yapeng, Marcus Bintz, Lídia Robert, et al.. (2019). Phenotypic heterogeneity and evolution of melanoma cells associated with targeted therapy resistance. PLoS Computational Biology. 15(6). e1007034–e1007034. 33 indexed citations
14.
Li, Guideng, Michael T. Bethune, Stephanie Wong, et al.. (2019). T cell antigen discovery via trogocytosis. Nature Methods. 16(2). 183–190. 107 indexed citations
15.
16.
Peng, Songming, Jesse M. Zaretsky, Michael T. Bethune, et al.. (2018). Sensitive, NonnDestructive Detection and Analysis of NeoantigennSpecific T Cell Populations from Tumors and Blood. SSRN Electronic Journal. 1 indexed citations
17.
Xu, Alexander M., Yapeng Su, Igor Antoshechkin, et al.. (2018). Integrated measurement of intracellular proteins and transcripts in single cells. Lab on a Chip. 18(21). 3251–3262. 14 indexed citations
18.
Su, Yapeng, Qihui Shi, & Wei Wei. (2017). Single cell proteomics in biomedicine: High‐dimensional data acquisition, visualization, and analysis. PROTEOMICS. 17(3-4). 66 indexed citations
19.
Xue, Min, et al.. (2016). Supramolecular Probes for Assessing Glutamine Uptake Enable Semi-Quantitative Metabolic Models in Single Cells. Journal of the American Chemical Society. 138(9). 3085–3093. 31 indexed citations
20.
Xue, Min, Wei Wei, Yapeng Su, et al.. (2015). Chemical Methods for the Simultaneous Quantitation of Metabolites and Proteins from Single Cells. Journal of the American Chemical Society. 137(12). 4066–4069. 79 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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