Yanguang Cao

2.5k total citations
92 papers, 1.8k citations indexed

About

Yanguang Cao is a scholar working on Oncology, Radiology, Nuclear Medicine and Imaging and Molecular Biology. According to data from OpenAlex, Yanguang Cao has authored 92 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Oncology, 31 papers in Radiology, Nuclear Medicine and Imaging and 27 papers in Molecular Biology. Recurrent topics in Yanguang Cao's work include Monoclonal and Polyclonal Antibodies Research (28 papers), CAR-T cell therapy research (12 papers) and Protein purification and stability (10 papers). Yanguang Cao is often cited by papers focused on Monoclonal and Polyclonal Antibodies Research (28 papers), CAR-T cell therapy research (12 papers) and Protein purification and stability (10 papers). Yanguang Cao collaborates with scholars based in United States, China and Hong Kong. Yanguang Cao's co-authors include William J. Jusko, Hua He, Dongfen Yuan, Yun Wu, Joseph P. Balthasar, Jianghong Fan, Yu Tang, Jie Zhao, Jiawei Zhou and Samuel K. Lai and has published in prestigious journals such as Nature Communications, Cancer Research and Advanced Drug Delivery Reviews.

In The Last Decade

Yanguang Cao

80 papers receiving 1.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yanguang Cao United States 23 637 442 384 355 237 92 1.8k
Jiang Liu United States 16 426 0.7× 446 1.0× 162 0.4× 227 0.6× 123 0.5× 43 1.5k
Simon Zhou United States 22 754 1.2× 498 1.1× 104 0.3× 310 0.9× 391 1.6× 71 2.5k
Amarnath Sharma United States 27 893 1.4× 994 2.2× 162 0.4× 605 1.7× 200 0.8× 62 2.8k
Sandip Kumar Roy India 18 1.1k 1.7× 765 1.7× 335 0.9× 213 0.6× 135 0.6× 75 2.5k
Jochem Alsenz Switzerland 29 731 1.1× 458 1.0× 351 0.9× 624 1.8× 179 0.8× 52 2.7k
James L. Weaver United States 23 530 0.8× 231 0.5× 91 0.2× 306 0.9× 222 0.9× 75 1.6k
Amitava Mitra United States 26 534 0.8× 222 0.5× 174 0.5× 91 0.3× 202 0.9× 52 1.7k
Yoshinori Inagaki Japan 28 1.1k 1.7× 416 0.9× 138 0.4× 238 0.7× 243 1.0× 125 2.7k
Michael Amantea United States 22 688 1.1× 756 1.7× 214 0.6× 118 0.3× 280 1.2× 48 2.6k
Ali Zarrinpar United States 33 1.5k 2.4× 428 1.0× 130 0.3× 248 0.7× 271 1.1× 105 4.4k

Countries citing papers authored by Yanguang Cao

Since Specialization
Citations

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

Fields of papers citing papers by Yanguang Cao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yanguang Cao

This figure shows the co-authorship network connecting the top 25 collaborators of Yanguang Cao. A scholar is included among the top collaborators of Yanguang Cao 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 Yanguang Cao. Yanguang Cao 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.
Zhang, Xu, Yawei Xu, Xiaoming He, et al.. (2025). New Qiangguyin activates Wnt/β-catenin pathway by down-regulating Notum to improve osteoporosis in rats. Journal of Ethnopharmacology. 347. 119745–119745.
2.
Cao, Yanguang & William J. Polacheck. (2025). New Approach Methodologies: What Clinical Pharmacologists Should Prepare For. Clinical Pharmacology & Therapeutics. 118(6). 1269–1272.
3.
Cao, Yanguang, et al.. (2025). Opportunities for machine learning and artificial intelligence in physiologically-based pharmacokinetic (PBPK) modeling. Advanced Drug Delivery Reviews. 227. 115716–115716.
4.
Zhong, Rui, et al.. (2025). Optimizing Maternal and Fetal Antibody Exposure and Dosing Regimens During Pregnancy Using a Physiologically Based Pharmacokinetic Model. Clinical Pharmacology & Therapeutics. 118(2). 394–407. 1 indexed citations
5.
6.
Zhou, Jiawei, et al.. (2024). Phenotypic switching as a non-genetic mechanism of resistance predicts antibody therapy regimens. iScience. 27(4). 109450–109450. 3 indexed citations
7.
Cao, Yanguang, et al.. (2023). Dissecting sources of variability in patient response to targeted therapy: anti-HER2 therapies as a case study. European Journal of Pharmaceutical Sciences. 186. 106467–106467.
8.
Liu, Jiang, et al.. (2023). Realizing the promise of Project Optimus: Challenges and emerging opportunities for dose optimization in oncology drug development. CPT Pharmacometrics & Systems Pharmacology. 13(5). 691–709. 23 indexed citations
9.
Kumar, Rukmini, et al.. (2023). Incorporating lesion-to-lesion heterogeneity into early oncology decision making. Frontiers in Immunology. 14. 1173546–1173546. 5 indexed citations
10.
11.
Zhou, Jiawei, Quefeng Li, & Yanguang Cao. (2021). Spatiotemporal Heterogeneity across Metastases and Organ-Specific Response Informs Drug Efficacy and Patient Survival in Colorectal Cancer. Cancer Research. 81(9). 2522–2533. 17 indexed citations
12.
Wessler, Timothy, et al.. (2021). Experimental Data and PBPK Modeling Quantify Antibody Interference in PEGylated Drug Carrier Delivery. Bulletin of Mathematical Biology. 83(12). 123–123. 7 indexed citations
13.
Cao, Yanguang, et al.. (2020). Pharmacokinetics and pharmacodynamics of therapeutic antibodies in tumors and tumor-draining lymph nodes. Mathematical Biosciences & Engineering. 18(1). 112–131. 5 indexed citations
14.
Tang, Yu & Yanguang Cao. (2020). Modeling the dynamics of antibody–target binding in living tumors. Scientific Reports. 10(1). 16764–16764. 11 indexed citations
15.
Zhou, Jiawei, Yutong Liu, Yubo Zhang, Quefeng Li, & Yanguang Cao. (2019). Modeling Tumor Evolutionary Dynamics to Predict Clinical Outcomes for Patients with Metastatic Colorectal Cancer: A Retrospective Analysis. Cancer Research. 80(3). 591–601. 18 indexed citations
16.
McSweeney, Morgan D., Lauren Price, Timothy Wessler, et al.. (2019). Overcoming anti-PEG antibody mediated accelerated blood clearance of PEGylated liposomes by pre-infusion with high molecular weight free PEG. Journal of Controlled Release. 311-312. 138–146. 70 indexed citations
17.
Zhao, Peng, Peng Wang, Shuyun Dong, et al.. (2019). Depletion of PD-1-positive cells ameliorates autoimmune disease. Nature Biomedical Engineering. 3(4). 292–305. 58 indexed citations
18.
McSweeney, Morgan D., Timothy Wessler, Lauren Price, et al.. (2018). A minimal physiologically based pharmacokinetic model that predicts anti-PEG IgG-mediated clearance of PEGylated drugs in human and mouse. Journal of Controlled Release. 284. 171–178. 66 indexed citations
19.
Cao, Yanguang & William J. Jusko. (2014). Survey of monoclonal antibody disposition in man utilizing a minimal physiologically-based pharmacokinetic model. Journal of Pharmacokinetics and Pharmacodynamics. 41(6). 571–580. 35 indexed citations
20.
Cao, Yanguang & William J. Jusko. (2014). Incorporating target-mediated drug disposition in a minimal physiologically-based pharmacokinetic model for monoclonal antibodies. Journal of Pharmacokinetics and Pharmacodynamics. 41(4). 375–387. 66 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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