Yao Zhao
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- Pituitary Gland Disorders and Treatments 44
- Growth Hormone and Insulin-like Growth Factors 21
- Health Informatics top 5%
- Genetics top 5%
- Glioma Diagnosis and Treatment 18
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- Radiomics and Machine Learning in Medical Imaging 11
- Neurology top 5%
- Vascular Malformations Diagnosis and Treatment 12
- Intracranial Aneurysms: Treatment and Complications 11
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- Adrenal and Paraganglionic Tumors 14
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- Meningioma and schwannoma management 8
- Journals
- Clinical Neurology and Neurosurgery (5 papers)World Neurosurgery (4 papers)Nature Communications (3 papers)
- Partner nations
- ChinaUnited StatesCanada
In The Last Decade
Yao Zhao
94 papers receiving 2.0k citations
Hit Papers
Peers
Comparison fields: 5 of 114
- Endocrinology, Diabetes and Metabolism 612
- Health Informatics 39
- Genetics 289
- Radiology, Nuclear Medicine and Imaging 615
- Neurology 287
Countries citing papers authored by Yao Zhao
This map shows the geographic impact of Yao Zhao'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 Yao Zhao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yao Zhao more than expected).
Fields of papers citing papers by Yao Zhao
This network shows the impact of papers produced by Yao Zhao. 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 Yao Zhao. The network helps show where Yao Zhao may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Yao Zhao, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 2 | |
| 2 | 2023 | 0 | |
| 3 | 2022 | 3 | |
| 4 | 2022 | 9 | |
| 5 | 2021 | 6 | |
| 6 | 2021 | 3 | |
| 7 | 2021 | 31 | |
| 8 | 2021 | 7 | |
| 9 | 2020 | 13 | |
| 10 | Deep learning radiomics can predict axillary lymph node status in early-stage breast cancerbreakdown → | 2020 | 402 |
| 11 | 2020 | 9 | |
| 12 | 2018 | 9 | |
| 13 | 2018 | 16 | |
| 14 | 2015 | 6 | |
| 15 | 2014 | 27 | |
| 16 | 2012 | 2 | |
| 17 | 2011 | 23 | |
| 18 | 2010 | 23 | |
| 19 | 2010 | 25 | |
| 20 | 2008 | 3 |
About Yao Zhao
Yao Zhao is a scholar working on Endocrinology, Diabetes and Metabolism, Genetics and Neurology, having authored 96 papers that have together received 2.0k indexed citations. Recurring topics across this work include Pituitary Gland Disorders and Treatments (44 papers), Growth Hormone and Insulin-like Growth Factors (21 papers), Glioma Diagnosis and Treatment (18 papers), Adrenal and Paraganglionic Tumors (14 papers), Vascular Malformations Diagnosis and Treatment (12 papers), Intracranial Aneurysms: Treatment and Complications (11 papers), Radiomics and Machine Learning in Medical Imaging (11 papers) and Meningioma and schwannoma management (8 papers). The work is most often cited by research in Endocrinology, Diabetes and Metabolism (612 citations), Health Informatics (39 citations) and Genetics (289 citations). Yao Zhao has collaborated with scholars based in China, United States and Canada. Frequent co-authors include Ying Mao, Jinhua Yu, Xuefei Shou, Shiqi Li, Yongfei Wang, Ming Shen, Yixin Hu, Yini Huang, Rushuang Mao and Yuanyuan Wang. Their work appears in journals such as Clinical Neurology and Neurosurgery, World Neurosurgery, Nature Communications, Neurosurgery and Pituitary.
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.