Qing Yi

13.8k total citations · 3 hit papers
197 papers, 9.8k citations indexed

About

Qing Yi is a scholar working on Immunology, Molecular Biology and Oncology. According to data from OpenAlex, Qing Yi has authored 197 papers receiving a total of 9.8k indexed citations (citations by other indexed papers that have themselves been cited), including 111 papers in Immunology, 64 papers in Molecular Biology and 63 papers in Oncology. Recurrent topics in Qing Yi's work include Immunotherapy and Immune Responses (66 papers), Multiple Myeloma Research and Treatments (58 papers) and T-cell and B-cell Immunology (52 papers). Qing Yi is often cited by papers focused on Immunotherapy and Immune Responses (66 papers), Multiple Myeloma Research and Treatments (58 papers) and T-cell and B-cell Immunology (52 papers). Qing Yi collaborates with scholars based in United States, China and Sweden. Qing Yi's co-authors include Jianfei Qian, Enguang Bi, Xingzhe Ma, Jing Yang, Maojie Yang, Lintao Liu, Larry W. Kwak, Yuhuan Zheng, Qiang Wang and Yong Lu and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and Journal of Clinical Investigation.

In The Last Decade

Qing Yi

193 papers receiving 9.7k citations

Hit Papers

Cholesterol Induces CD8+ ... 2019 2026 2021 2023 2019 2021 2020 200 400 600

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Qing Yi 4.9k 3.8k 3.6k 1.8k 1.7k 197 9.8k
Veronika Sexl 4.4k 0.9× 3.3k 0.9× 3.6k 1.0× 1.5k 0.8× 1.1k 0.6× 183 9.5k
Eduardo M. Sotomayor 6.6k 1.4× 3.9k 1.0× 4.9k 1.4× 626 0.3× 989 0.6× 198 11.1k
William Matsui 1.7k 0.3× 6.1k 1.6× 5.3k 1.5× 2.5k 1.4× 1.7k 1.0× 154 10.8k
Ivan Borrello 7.3k 1.5× 3.3k 0.9× 7.3k 2.0× 3.5k 1.9× 532 0.3× 148 13.0k
R. Eric Davis 2.7k 0.6× 3.8k 1.0× 2.6k 0.7× 1.3k 0.7× 1.4k 0.8× 161 8.4k
Christoph Huber 5.0k 1.0× 3.3k 0.9× 3.5k 1.0× 791 0.4× 504 0.3× 155 8.6k
David Avigan 3.7k 0.8× 3.0k 0.8× 2.9k 0.8× 2.6k 1.4× 702 0.4× 208 7.5k
Amnon Peled 4.7k 1.0× 2.9k 0.8× 4.7k 1.3× 3.2k 1.7× 705 0.4× 138 10.3k
Joost J. van den Oord 1.8k 0.4× 2.8k 0.7× 1.9k 0.5× 854 0.5× 1.3k 0.8× 167 7.8k
Gert Riethmüller 3.3k 0.7× 3.1k 0.8× 5.1k 1.4× 572 0.3× 2.2k 1.3× 112 10.4k

Countries citing papers authored by Qing Yi

Since Specialization
Citations

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

Fields of papers citing papers by Qing Yi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Qing Yi

This figure shows the co-authorship network connecting the top 25 collaborators of Qing Yi. A scholar is included among the top collaborators of Qing Yi 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 Qing Yi. Qing Yi 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, Qin, Upasana Ray, Iqbal Mahmud, et al.. (2025). Targeting caseinolytic mitochondrial matrix peptidase, a novel contributor to the pathobiology of high-risk multiple myeloma. Blood. 145(22). 2614–2629. 2 indexed citations
2.
Yi, Qing, et al.. (2025). Table tennis coaching system based on a multimodal large language model with a table tennis knowledge base. PLoS ONE. 20(2). e0317839–e0317839. 2 indexed citations
3.
Xian, Miao, Qiang Wang, Liuling Xiao, et al.. (2024). Leukocyte immunoglobulin-like receptor B1 (LILRB1) protects human multiple myeloma cells from ferroptosis by maintaining cholesterol homeostasis. Nature Communications. 15(1). 5767–5767. 15 indexed citations
4.
Xiao, Liuling, et al.. (2024). Lipid peroxidation of immune cells in cancer. Frontiers in Immunology. 14. 1322746–1322746. 18 indexed citations
5.
Liu, Xiaoshan, Guolan Fu, Guiqiang Liu, et al.. (2021). Nano-slit assisted high-Q photonic resonant perfect absorbers. Optics Express. 29(4). 5270–5270. 7 indexed citations
6.
Zhong, Wenbin, Qun Niu, Xiaoqin Feng, et al.. (2021). ORP4L is a prerequisite for the induction of T-cell leukemogenesis associated with human T-cell leukemia virus 1. Blood. 139(7). 1052–1065. 9 indexed citations
7.
Wang, Qiang, Zhijuan Lin, Zhuo Wang, et al.. (2021). RARγ activation sensitizes human myeloma cells to carfilzomib treatment through the OAS-RNase L innate immune pathway. Blood. 139(1). 59–72. 16 indexed citations
8.
Su, Pan, Qiang Wang, Enguang Bi, et al.. (2020). Enhanced Lipid Accumulation and Metabolism Are Required for the Differentiation and Activation of Tumor-Associated Macrophages. Cancer Research. 80(7). 1438–1450. 360 indexed citations breakdown →
9.
Hsiang-Chi, Tseng, Wei Xiong, Saiaditya Badeti, et al.. (2020). Efficacy of anti-CD147 chimeric antigen receptors targeting hepatocellular carcinoma. Nature Communications. 11(1). 4810–4810. 140 indexed citations
10.
Ma, Xingzhe, Enguang Bi, Yong Lu, et al.. (2019). Cholesterol Induces CD8+ T Cell Exhaustion in the Tumor Microenvironment. Cell Metabolism. 30(1). 143–156.e5. 697 indexed citations breakdown →
11.
Ma, Xingzhe, Enguang Bi, Chunjian Huang, et al.. (2018). Cholesterol negatively regulates IL-9–producing CD8+ T cell differentiation and antitumor activity. The Journal of Experimental Medicine. 215(6). 1555–1569. 118 indexed citations
12.
Lu, Yong, Qiang Wang, Gang Xue, et al.. (2018). Th9 Cells Represent a Unique Subset of CD4+ T Cells Endowed with the Ability to Eradicate Advanced Tumors. Cancer Cell. 33(6). 1048–1060.e7. 121 indexed citations
13.
Li, Rongying, Jeongkyung Lee, Mi Sun Kim, et al.. (2014). PD-L1–Driven Tolerance Protects Neurogenin3-Induced Islet Neogenesis to Reverse Established Type 1 Diabetes in NOD Mice. Diabetes. 64(2). 529–540. 20 indexed citations
14.
Zhang, Liang, Lan V. Pham, Kate J. Newberry, et al.. (2013). In Vitro and In Vivo Therapeutic Efficacy of Carfilzomib in Mantle Cell Lymphoma: Targeting the Immunoproteasome. Molecular Cancer Therapeutics. 12(11). 2494–2504. 22 indexed citations
15.
He, Jin, Zhiqiang Liu, Yuhuan Zheng, et al.. (2012). p38 MAPK in Myeloma Cells Regulates Osteoclast and Osteoblast Activity and Induces Bone Destruction. Cancer Research. 72(24). 6393–6402. 69 indexed citations
16.
Zhang, Liang, Jing Yang, Jianfei Qian, et al.. (2012). Role of the microenvironment in mantle cell lymphoma: IL-6 is an important survival factor for the tumor cells. Blood. 120(18). 3783–3792. 88 indexed citations
17.
Ma, Wencai, Michael Wang, Zhiqiang Wang, et al.. (2010). Effect of Long-term Storage in TRIzol on Microarray-Based Gene Expression Profiling. Cancer Epidemiology Biomarkers & Prevention. 19(10). 2445–2452. 35 indexed citations
18.
Yang, Jing, Michele Wezeman, Xiang Zhang, et al.. (2007). Human C-Reactive Protein Binds Activating Fcγ Receptors and Protects Myeloma Tumor Cells from Apoptosis. Cancer Cell. 12(3). 252–265. 97 indexed citations
19.
Yi, Qing. (2003). Information Query System Based on GSM Short Message. Jisuanji yingyong yanjiu.

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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