Ze Yu

479 total citations
31 papers, 293 citations indexed

About

Ze Yu is a scholar working on Pharmacology, Surgery and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Ze Yu has authored 31 papers receiving a total of 293 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Pharmacology, 5 papers in Surgery and 5 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Ze Yu's work include Treatment of Major Depression (5 papers), Blood Pressure and Hypertension Studies (3 papers) and Schizophrenia research and treatment (3 papers). Ze Yu is often cited by papers focused on Treatment of Major Depression (5 papers), Blood Pressure and Hypertension Studies (3 papers) and Schizophrenia research and treatment (3 papers). Ze Yu collaborates with scholars based in China, Australia and Canada. Ze Yu's co-authors include Fei Gao, Xin Hao, Jihui Chen, Ajing Xu, Jinyuan Zhang, Jinyuan Zhang, Fei Gao, Zeyuan Wang, Yu Peng and Xin Wei and has published in prestigious journals such as Cancer, Scientific Reports and BioMed Research International.

In The Last Decade

Ze Yu

29 papers receiving 286 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ze Yu China 10 49 44 41 36 34 31 293
Stephen A. Goldman United States 7 45 0.9× 63 1.4× 73 1.8× 24 0.7× 13 0.4× 20 407
Erica A. Voss United States 11 38 0.8× 14 0.3× 44 1.1× 38 1.1× 76 2.2× 27 500
Louis Létinier France 9 29 0.6× 24 0.5× 48 1.2× 18 0.5× 13 0.4× 22 285
He Sun China 13 87 1.8× 61 1.4× 39 1.0× 18 0.5× 20 0.6× 32 486
Joseph M. Tonning United States 9 75 1.5× 48 1.1× 11 0.3× 98 2.7× 45 1.3× 13 503
Johan Hopstadius Sweden 7 86 1.8× 61 1.4× 23 0.6× 116 3.2× 52 1.5× 8 506
Teun M. Post Netherlands 9 61 1.2× 54 1.2× 17 0.4× 47 1.3× 11 0.3× 18 551
Mónica Muñoz United States 14 23 0.5× 41 0.9× 19 0.5× 36 1.0× 14 0.4× 45 485
Vaishali Patadia United States 9 92 1.9× 69 1.6× 25 0.6× 116 3.2× 27 0.8× 13 525
Mou-Ze Liu China 9 42 0.9× 21 0.5× 19 0.5× 14 0.4× 17 0.5× 13 322

Countries citing papers authored by Ze Yu

Since Specialization
Citations

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

Fields of papers citing papers by Ze Yu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ze Yu

This figure shows the co-authorship network connecting the top 25 collaborators of Ze Yu. A scholar is included among the top collaborators of Ze Yu 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 Ze Yu. Ze Yu 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.
2.
Fu, Ran, Jing Yu, Donghan Wang, et al.. (2024). Machine learning-based prediction of sertraline concentration in patients with depression through therapeutic drug monitoring. Frontiers in Pharmacology. 15. 1289673–1289673. 6 indexed citations
3.
4.
Yang, Shiguang, et al.. (2024). Tectochrysin ameliorates dextran sulfate sodium-induced chronic colitis by regulating the intestinal flora and inflammatory responses. Food Bioscience. 60. 104110–104110. 1 indexed citations
5.
Liu, Li, et al.. (2024). Imatinib adherence prediction using machine learning approach in patients with gastrointestinal stromal tumor. Cancer. 131(1). e35548–e35548. 2 indexed citations
6.
Zhang, Jinyuan, Jing Yu, Ze Yu, et al.. (2024). Predicting quetiapine dose in patients with depression using machine learning techniques based on real-world evidence. Annals of General Psychiatry. 23(1). 5–5. 4 indexed citations
7.
Yu, Ze, Xuxiao Ye, Jinyuan Zhang, et al.. (2024). Personalized venlafaxine dose prediction using artificial intelligence technology: a retrospective analysis based on real-world data. International Journal of Clinical Pharmacy. 46(4). 926–936. 3 indexed citations
8.
Yu, Ze, Ping Li, Yan Liu, et al.. (2023). The effect of Shengmai injection in patients with coronary heart disease in real world and its personalized medicine research using machine learning techniques. Frontiers in Pharmacology. 14. 1208621–1208621. 4 indexed citations
9.
Zhang, Jinyuan, Lin Yang, Chunhua Zhou, et al.. (2023). A machine learning model for predicting blood concentration of quetiapine in patients with schizophrenia and depression based on real‐world data. British Journal of Clinical Pharmacology. 89(9). 2714–2725. 8 indexed citations
10.
Yu, Ze, et al.. (2022). Low Hemoglobin‐to‐Red Cell Distribution Width Ratio Is Associated with Mortality in Patients with HBV‐Related Decompensated Cirrhosis. BioMed Research International. 2022(1). 5754790–5754790. 13 indexed citations
11.
Zhang, Qiwen, Ze Yu, Xiaojian Zhang, et al.. (2022). A Prediction Model for Tacrolimus Daily Dose in Kidney Transplant Recipients With Machine Learning and Deep Learning Techniques. Frontiers in Medicine. 9. 813117–813117. 23 indexed citations
12.
Yu, Ze, Xuan Ye, Hongyue Liu, et al.. (2022). Predicting Lapatinib Dose Regimen Using Machine Learning and Deep Learning Techniques Based on a Real-World Study. Frontiers in Oncology. 12. 893966–893966. 19 indexed citations
13.
Yu, Ze, Huanhuan Ji, Ping Wei, et al.. (2021). Predicting Adverse Drug Events in Chinese Pediatric Inpatients With the Associated Risk Factors: A Machine Learning Study. Frontiers in Pharmacology. 12. 659099–659099. 27 indexed citations
14.
Huang, Xiaohui, Ze Yu, Xin Hao, et al.. (2021). An Ensemble Model for Prediction of Vancomycin Trough Concentrations in Pediatric Patients. Drug Design Development and Therapy. Volume 15. 1549–1559. 31 indexed citations
15.
Zheng, Ping, Ze Yu, Liren Li, et al.. (2021). Predicting Blood Concentration of Tacrolimus in Patients With Autoimmune Diseases Using Machine Learning Techniques Based on Real-World Evidence. Frontiers in Pharmacology. 12. 727245–727245. 25 indexed citations
16.
He, Xia, Mengting Li, Hong Liang, et al.. (2021). A Risk Scoring Model for High-Dose Methotrexate-Induced Liver Injury in Children With Acute Lymphoblastic Leukemia Based on Gene Polymorphism Study. Frontiers in Pharmacology. 12. 726229–726229. 8 indexed citations
17.
Liu, Yan, Jihui Chen, You Yin, et al.. (2021). An ensemble learning based framework to estimate warfarin maintenance dose with cross-over variables exploration on incomplete data set. Computers in Biology and Medicine. 131. 104242–104242. 18 indexed citations
18.
Cai, Zeling, De Cai, Ruiwen Wang, et al.. (2021). Cost-effectiveness of CYP2C19 genotyping to guide antiplatelet therapy for acute minor stroke and high-risk transient ischemic attack. Scientific Reports. 11(1). 7383–7383. 18 indexed citations
19.
Huang, Ningbo, Gang Zhou, Mengli Zhang, Meng Zhang, & Ze Yu. (2021). Modelling the Latent Semantics of Diffusion Sources in Information Cascade Prediction. Computational Intelligence and Neuroscience. 2021(1). 7880215–7880215. 1 indexed citations
20.
Yu, Ze & Jason Gu. (2010). Image-based grasp synthesis using Genetic Algorithm. 24. 6697–6702. 1 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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