ResearcherQingdao UniversityActive in the last 2 years
Jingyu Cao
Matched on ORCID iD 0000-0003-3355-2031; OpenAlex author ID A5057632419. Sources: OpenAlex.
Their work
- Papers
- 6 (first author 0, last author 1)
- Trials
- 0
- Grants
- 0
- Labs led
- 0
- Collaborators
- 21
2011: 1 paper · 2012: none · 2013: none · 2014: none · 2015: none · 2016: none · 2017: none · 2018: none · 2019: none · 2020: none · 2021: none · 2022: 2 papers · 2023: none · 2024: none · 2025: 3 papers
Papers 6
- Machine learning model reveals the risk, prognosis, and drug response of histamine-related signatures in pancreatic cancer. Co-author DOI 10.1007/s12672-025-01910-y↗
- Multiparametric quantitative diffusion weighted magnetic resonance imaging can effectively predict the response to neoadjuvant therapy in borderline resectable… Co-author DOI 10.1016/j.ejrad.2024.111893↗
- A machine learning model based on preoperative multiparametric quantitative DWI can effectively predict the survival and recurrence risk of pancreatic ductal a… Co-author DOI 10.1186/s13244-025-01915-9↗
- Cancer-associated fibroblast-secreted miR-421 promotes pancreatic cancer by regulating the SIRT3/H3K9Ac/HIF-1α axis. Last author DOI 10.1002/kjm2.12590↗
- LINC00960 regulates cell proliferation and glycolysis in pancreatic cancer through the miR-326-3p/TUFT1/AKT-mTOR axis. Co-author DOI 10.1002/kjm2.12594↗
- Slug enhances invasion ability of pancreatic cancer cells through upregulation of matrix metalloproteinase-9 and actin cytoskeleton remodeling. Co-author DOI 10.1038/labinvest.2010.201↗
Closest collaborators
The 21 strongest of 21 by collab_strength@1: shared papers, trials and grants, recent and small-team work weighing most.
- Piaoe Zeng · 2 shared papers, 2025 · strength 0.52
- Paper Multiparametric quantitative diffusion weighted magnetic resonance imaging can effectively predict the response to neoa… DOI 10.1016/j.ejrad.2024.111893↗
- Paper A machine learning model based on preoperative multiparametric quantitative DWI can effectively predict the survival an… DOI 10.1186/s13244-025-01915-9↗
- Chao Qu · 2 shared papers, 2025 · strength 0.52
- Paper Multiparametric quantitative diffusion weighted magnetic resonance imaging can effectively predict the response to neoa… DOI 10.1016/j.ejrad.2024.111893↗
- Paper A machine learning model based on preoperative multiparametric quantitative DWI can effectively predict the survival an… DOI 10.1186/s13244-025-01915-9↗
- Changlei Li · 2 shared papers, 2025 · strength 0.52
- Paper Machine learning model reveals the risk, prognosis, and drug response of histamine-related signatures in pancreatic can… DOI 10.1007/s12672-025-01910-y↗
- Paper A machine learning model based on preoperative multiparametric quantitative DWI can effectively predict the survival an… DOI 10.1186/s13244-025-01915-9↗
- Huishu Yuan · 2 shared papers, 2025 · strength 0.52
- Paper Multiparametric quantitative diffusion weighted magnetic resonance imaging can effectively predict the response to neoa… DOI 10.1016/j.ejrad.2024.111893↗
- Paper A machine learning model based on preoperative multiparametric quantitative DWI can effectively predict the survival an… DOI 10.1186/s13244-025-01915-9↗
- Weidong Guo · 2 shared papers, 2022 · strength 0.28
- Shanglong Liu · 2 shared papers, 2022 · strength 0.28
- T. T. Qu · 2 shared papers, 2022 · strength 0.28
- Jing‐Hao Lei · 2 shared papers, 2022 · strength 0.28
- Hanxiang Zhan · 2 shared papers, 2022 · strength 0.28
- Yukun Liu · 1 shared paper, 2025 · strength 0.27
- Zusen Wang · 1 shared paper, 2025 · strength 0.27
- Chao Qu · 1 shared paper, 2025 · strength 0.27
- Dianrong Xiu · 1 shared paper, 2025 · strength 0.27
- Dianrong Xiu · 1 shared paper, 2025 · strength 0.26
- Bin Zhou · 1 shared paper, 2022 · strength 0.14
- Xiao Hu · 1 shared paper, 2022 · strength 0.14
- Bin Zhou · 1 shared paper, 2022 · strength 0.14
- Xiao Hu · 1 shared paper, 2022 · strength 0.14
- Dong Chen · 1 shared paper, 2011 · strength 0.03
- Kejun Zhang · 1 shared paper, 2011 · strength 0.03
- Dongsheng Wang · 1 shared paper, 2011 · strength 0.03
How to reach this person
A chain of introductions through people who have published, run trials or held grants together (warm_path@1: fewest links first, then the strongest). Built from public records only; no contact details are kept.