Ying Chi

9.5k total citations · 1 hit paper
131 papers, 5.5k citations indexed

About

Ying Chi is a scholar working on Molecular Biology, Genetics and Immunology. According to data from OpenAlex, Ying Chi has authored 131 papers receiving a total of 5.5k indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Molecular Biology, 30 papers in Genetics and 23 papers in Immunology. Recurrent topics in Ying Chi's work include Mesenchymal stem cell research (29 papers), MicroRNA in disease regulation (10 papers) and Cancer-related molecular mechanisms research (9 papers). Ying Chi is often cited by papers focused on Mesenchymal stem cell research (29 papers), MicroRNA in disease regulation (10 papers) and Cancer-related molecular mechanisms research (9 papers). Ying Chi collaborates with scholars based in China, United States and Singapore. Ying Chi's co-authors include Kenneth J. Pienta, Robert D. Loberg, Zhongchao Han, Hernan Roca, Zachary S. Varsos, Linda A. Snyder, Sudha Sud, Matthew Craig, Chris K. Neeley and Yan Li and has published in prestigious journals such as Journal of Biological Chemistry, Journal of Clinical Investigation and Nature Communications.

In The Last Decade

Ying Chi

127 papers receiving 5.4k citations

Hit Papers

Human prostate cancer metastases target the hematopoietic... 2011 2026 2016 2021 2011 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ying Chi China 38 1.8k 1.7k 1.5k 973 782 131 5.5k
Thomas F.E. Barth Germany 46 2.0k 1.2× 1.6k 1.0× 1.7k 1.1× 1.2k 1.2× 925 1.2× 236 7.0k
Rifat Hamoudi United Kingdom 43 2.2k 1.3× 2.7k 1.6× 1.3k 0.8× 1.0k 1.1× 1.5k 2.0× 257 7.8k
Amit Verma United States 49 1.8k 1.0× 3.9k 2.4× 1.3k 0.9× 1.2k 1.2× 1.1k 1.4× 265 8.0k
Sergio Rutella Italy 47 2.0k 1.1× 2.3k 1.4× 3.0k 2.0× 1.1k 1.1× 530 0.7× 239 8.2k
Annette Schmitt‐Graeff Germany 47 1.4k 0.8× 2.0k 1.2× 2.0k 1.3× 1.4k 1.5× 407 0.5× 195 6.6k
Selvarangan Ponnazhagan United States 45 1.5k 0.8× 2.8k 1.7× 1.1k 0.7× 704 0.7× 492 0.6× 127 5.2k
Shinya Kimura Japan 45 2.1k 1.2× 2.6k 1.6× 998 0.7× 1.3k 1.3× 683 0.9× 427 7.1k
Andrew L. Feldman United States 49 3.6k 2.0× 1.9k 1.1× 2.0k 1.3× 1.3k 1.4× 1.1k 1.4× 228 8.8k
Norio Komatsu Japan 44 1.2k 0.7× 2.6k 1.5× 1.4k 0.9× 1.8k 1.8× 525 0.7× 384 6.7k

Countries citing papers authored by Ying Chi

Since Specialization
Citations

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

Fields of papers citing papers by Ying Chi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ying Chi

This figure shows the co-authorship network connecting the top 25 collaborators of Ying Chi. A scholar is included among the top collaborators of Ying Chi 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 Ying Chi. Ying Chi 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.
Zhao, Wenyi, Ying Chi, Gang Pan, et al.. (2025). TRAIT: A Comprehensive Database for T-cell Receptor–antigen Interactions. Genomics Proteomics & Bioinformatics. 23(3). 1 indexed citations
2.
Ma, Junyan, Qiao Qiao, Xiaojuan Zhu, et al.. (2025). Development and evaluation of MMIRA-CRISPR/Cas13a-MBQD assay for the detection of influenza A/B viruses and SARS-CoV-2. Future Virology. 20(1-2). 9–17. 1 indexed citations
3.
Qian, Jingyang, Jie Liao, Ziqi Liu, et al.. (2023). Reconstruction of the cell pseudo-space from single-cell RNA sequencing data with scSpace. Nature Communications. 14(1). 2484–2484. 26 indexed citations
4.
Chi, Ying, Xian‐Sheng Hua, Jian Wu, et al.. (2022). TSNAdb v2.0: The Updated Version of Tumor-Specific Neoantigen Database. Genomics Proteomics & Bioinformatics. 21(2). 259–266. 14 indexed citations
5.
Guo, Xiling, Yin Chen, Ling Wang, et al.. (2021). In vitro inactivation of SARS-CoV-2 by commonly used disinfection products and methods. Scientific Reports. 11(1). 2418–2418. 81 indexed citations
6.
Zhou, Zhan, Wenyi Zhao, Xun Gu, et al.. (2021). TSNAD v2.0: A one-stop software solution for tumor-specific neoantigen detection. Computational and Structural Biotechnology Journal. 19. 4510–4516. 18 indexed citations
7.
Xu, Minfeng, Yu Wang, Ying Chi, & Xian‐Sheng Hua. (2020). Training Liver Vessel Segmentation Deep Neural Networks on Noisy Labels from Contrast CT Imaging. 1552–1555. 18 indexed citations
8.
Wei, Yimeng, Leisheng Zhang, Ying Chi, et al.. (2020). High‐efficient generation of VCAM‐1+ mesenchymal stem cells with multidimensional superiorities in signatures and efficacy on aplastic anaemia mice. Cell Proliferation. 53(8). e12862–e12862. 43 indexed citations
9.
Xu, Jing, Wei Liu, Chao Liu, et al.. (2019). Concept Detection based on Multi-label Classification and Image Captioning Approach - DAMO at ImageCLEF 2019.. CLEF (Working Notes). 4 indexed citations
10.
Li, Juanjuan, Fengxia Ma, Youwei Wang, et al.. (2017). Knockdown of IL-8 Provoked Premature Senescence of Placenta-Derived Mesenchymal Stem Cells. Stem Cells and Development. 26(12). 912–931. 15 indexed citations
11.
Wang, Qiang, et al.. (2015). [Comparison Test Between PM2.5 Continuous Monitoring System and Manual Sampling Analysis for PM2.5 in Ambient Air].. PubMed. 36(5). 1538–43.
12.
Chen, Shihao, Li‐Fen Lee, Timothy S. Fisher, et al.. (2014). Combination of 4-1BB Agonist and PD-1 Antagonist Promotes Antitumor Effector/Memory CD8 T Cells in a Poorly Immunogenic Tumor Model. Cancer Immunology Research. 3(2). 149–160. 207 indexed citations
13.
Chen, Fang, Kang Zhou, Lei Zhang, et al.. (2013). Mesenchymal Stem Cells Induce Granulocytic Differentiation of Acute Promyelocytic Leukemic Cells via IL-6 and MEK/ERK Pathways. Stem Cells and Development. 22(13). 1955–1967. 16 indexed citations
14.
Zhou, Yang, Zhibo Han, Yue Ji, et al.. (2013). CD106 Identifies a Subpopulation of Mesenchymal Stem Cells with Unique Immunomodulatory Properties. PLoS ONE. 8(3). e59354–e59354. 175 indexed citations
15.
Yang, Zhouxin, et al.. (2013). [IL-1β promotes the hematopoietic support of human umbilical cord mesenchymal stem cells].. PubMed. 21(4). 1005–9. 2 indexed citations
16.
Chen, Ke, Ding Wang, Wei Du, et al.. (2010). Human umbilical cord mesenchymal stem cells hUC-MSCs exert immunosuppressive activities through a PGE2-dependent mechanism. Clinical Immunology. 135(3). 448–458. 159 indexed citations
17.
Wang, Ding, Ke Chen, Wei Du, et al.. (2010). CD14+ monocytes promote the immunosuppressive effect of human umbilical cord matrix stem cells. Experimental Cell Research. 316(15). 2414–2423. 43 indexed citations
18.
Li, Xin, Robert D. Loberg, Jinhui Liao, et al.. (2009). A Destructive Cascade Mediated by CCL2 Facilitates Prostate Cancer Growth in Bone. Cancer Research. 69(4). 1685–1692. 131 indexed citations
19.
Loberg, Robert D., Ying Chi, Chris K. Neeley, et al.. (2007). Targeting CCL2 with Systemic Delivery of Neutralizing Antibodies Induces Prostate Cancer Tumor RegressionIn vivo. Cancer Research. 67(19). 9417–9424. 283 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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