Ling Xu

6.5k total citations · 2 hit papers
98 papers, 3.1k citations indexed

About

Ling Xu is a scholar working on Immunology, Oncology and Molecular Biology. According to data from OpenAlex, Ling Xu has authored 98 papers receiving a total of 3.1k indexed citations (citations by other indexed papers that have themselves been cited), including 43 papers in Immunology, 42 papers in Oncology and 28 papers in Molecular Biology. Recurrent topics in Ling Xu's work include Immune Cell Function and Interaction (29 papers), CAR-T cell therapy research (25 papers) and T-cell and B-cell Immunology (13 papers). Ling Xu is often cited by papers focused on Immune Cell Function and Interaction (29 papers), CAR-T cell therapy research (25 papers) and T-cell and B-cell Immunology (13 papers). Ling Xu collaborates with scholars based in China, United States and Montenegro. Ling Xu's co-authors include Yangqiu Li, Dongliang Yang, Mengji Lu, Xin Zheng, Jia Liu, Dimitri Kasakovski, Shaohua Chen, Jiaxiong Tan, Chengwu Zeng and Lijian Yang and has published in prestigious journals such as Journal of Biological Chemistry, SHILAP Revista de lepidopterología and Blood.

In The Last Decade

Ling Xu

92 papers receiving 3.1k citations

Hit Papers

Liver injury during highly pathogenic human coronavirus i... 2020 2026 2022 2024 2020 2023 200 400 600

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ling Xu China 27 977 937 861 751 333 98 3.1k
Duck Cho South Korea 29 1.3k 1.3× 1.0k 1.1× 464 0.5× 635 0.8× 370 1.1× 205 3.0k
Chyi‐Chia Richard Lee United States 40 1.8k 1.8× 2.0k 2.1× 2.0k 2.3× 474 0.6× 496 1.5× 114 5.4k
Su‐Hyung Park South Korea 36 1.9k 1.9× 1.2k 1.3× 818 1.0× 894 1.2× 946 2.8× 116 4.2k
Philippe Delvenne Belgium 30 596 0.6× 563 0.6× 848 1.0× 270 0.4× 699 2.1× 113 2.9k
Lynn Sorbara United States 32 762 0.8× 1.7k 1.8× 686 0.8× 184 0.2× 381 1.1× 58 4.0k
Hossein Khorramdelazad Iran 29 905 0.9× 665 0.7× 662 0.8× 237 0.3× 367 1.1× 124 2.5k
David B. Sykes United States 30 1.1k 1.1× 649 0.7× 1.7k 2.0× 222 0.3× 283 0.8× 118 3.5k
Nienke Vrisekoop Netherlands 25 2.1k 2.1× 742 0.8× 868 1.0× 244 0.3× 401 1.2× 68 3.6k
Christoph Hess Switzerland 37 3.0k 3.1× 1.3k 1.4× 1.6k 1.9× 317 0.4× 559 1.7× 95 5.5k
Fridtjof Lund‐Johansen Norway 35 2.4k 2.5× 706 0.8× 1.1k 1.3× 656 0.9× 289 0.9× 94 4.9k

Countries citing papers authored by Ling Xu

Since Specialization
Citations

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

Fields of papers citing papers by Ling Xu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ling Xu

This figure shows the co-authorship network connecting the top 25 collaborators of Ling Xu. A scholar is included among the top collaborators of Ling Xu 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 Ling Xu. Ling Xu 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.
Pan, Zhenzhen, et al.. (2025). CRISPR-Cas for hepatitis virus: a systematic review and meta-analysis of diagnostic test accuracy studies. Frontiers in Microbiology. 16. 1509890–1509890.
2.
Yu, Xibao, et al.. (2024). CD69 is a Promising Immunotherapy and Prognosis Prediction Target in Cancer. ImmunoTargets and Therapy. Volume 13. 1–14. 14 indexed citations
3.
Wang, Hua, Boyun Liang, Sumeng Li, et al.. (2024). Omicron breakthrough infections after triple‐dose inactivated COVID‐19 vaccination: A comprehensive analysis of antibody and T‐cell responses. Immunology. 172(2). 313–327. 1 indexed citations
5.
Xu, Ling, Yu Zhang, Xudong Liu, et al.. (2024). Bioinformatics Analysis of Gene Modules and Key Genes for the Early Diagnosis of Gastric Cancer. 4(1). 24–36.
6.
Xu, Ling, Yu Zhang, Xudong Liu, et al.. (2024).  Mining of Gene Modules and Identification of Key Genes for Early Diagnosis of Gastric Cancer. 13(1). 26–38. 1 indexed citations
8.
Chen, Cunte, Si‐Yang Maggie Liu, Yedan Chen, et al.. (2022). Identification of TCR rearrangements specific for genetic alterations in EGFR-mutated non-small cell lung cancer: results from the ADJUVANT-CTONG1104 trial. Cancer Immunology Immunotherapy. 72(5). 1261–1272. 1 indexed citations
9.
Jin, Ying, Xiaoyu An, Binchen Mao, et al.. (2022). Different syngeneic tumors show distinctive intrinsic tumor-immunity and mechanisms of actions (MOA) of anti-PD-1 treatment. Scientific Reports. 12(1). 3278–3278. 40 indexed citations
10.
Yao, Danlin, Ling Xu, Jing Lai, et al.. (2020). Increased Expression of TIGIT/CD57 in Peripheral Blood/Bone Marrow NK Cells in Patients with Chronic Myeloid Leukemia. BioMed Research International. 2020(1). 9531549–9531549. 14 indexed citations
11.
Jin, Zhenyi, Yun Zhao, Jie Chen, et al.. (2020). Higher TIGIT + CD226 - γδ T cells in Patients with Acute Myeloid Leukemia. Immunological Investigations. 51(1). 40–50. 34 indexed citations
12.
Yao, Danlin, Dimitri Kasakovski, Yikai Zhang, et al.. (2019). Age related human T cell subset evolution and senescence. Immunity & Ageing. 16(1). 24–24. 142 indexed citations
13.
Wang, Xu, Shuai Lu, Ling Xu, et al.. (2018). Alteration of gene expression profile in CD3<sup>+</sup> T-cells after downregulating MALT1. ImmunoTargets and Therapy. Volume 7. 77–81. 1 indexed citations
14.
Tan, Jiaxiong, Shaohua Chen, Yuhong Lu, et al.. (2017). Higher PD-1 expression concurrent with exhausted CD8+ T cells in patients with de novo acute myeloid leukemia. Chinese Journal of Cancer Research. 29(5). 463–470. 70 indexed citations
15.
Shi, Li, Shaohua Chen, Xianfeng Zha, et al.. (2015). Enhancement of the TCRζ Expression, Polyclonal Expansion, and Activation of T Cells from Patients with Acute Myeloid Leukemia After IL-2, IL-7, and IL-12 Induction. DNA and Cell Biology. 34(7). 481–488. 11 indexed citations
16.
Zeng, Chengwu, Yan Xu, Ling Xu, et al.. (2014). Inhibition of long non-coding RNA NEAT1 impairs myeloid differentiation in acute promyelocytic leukemia cells. BMC Cancer. 14(1). 693–693. 170 indexed citations
17.
Shi, Li, Shaohua Chen, Yuhong Lu, et al.. (2013). Changes in the MALT1-A20-NF-κB expression pattern may be related to T cell dysfunction in AML. Cancer Cell International. 13(1). 37–37. 15 indexed citations
18.
Xu, Ling, Zhiyong Ke, Libin Huang, et al.. (2011). Five Chinese Pediatric Patients with Leukemias Possibly Arising from Immature Natural Killer Cells: Clinical Features and Courses. Pediatric Hematology and Oncology. 28(3). 187–193. 5 indexed citations
19.
Wang, Zengyan, Ling Xu, & Fang-Fang He. (2010). Embryo vitrification affects the methylation of the H19/Igf2 differentially methylated domain and the expression of H19 and Igf2. Fertility and Sterility. 93(8). 2729–2733. 89 indexed citations
20.
Luo, Xue‐Qun, et al.. (2008). High-Dose Chemotherapy without Stem Cell Transplantation for Refractory Childhood Systemic Lupus Erythematosus. Chemotherapy. 54(5). 331–335. 6 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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