Kan Deng
Impact in
-
- Pituitary Gland Disorders and Treatments
- Growth Hormone and Insulin-like Growth Factors
- Adrenal Hormones and Disorders
- Genetics top 5%
- Glioma Diagnosis and Treatment
Papers in
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- Pituitary Gland Disorders and Treatments 85
- Growth Hormone and Insulin-like Growth Factors 11
-
- TGF-β signaling in diseases 4
- Co-authors
- Yong Yao (98 shared papers)Renzhi Wang (70 shared papers)Bing Xing (54 shared papers)Andrew Moore (4 shared papers)Wei Lian (44 shared papers)Ming Feng (46 shared papers)Xinjie Bao (44 shared papers)Xiaopeng Guo (28 shared papers)
- Journals
- Frontiers in Endocrinology (18 papers)World Neurosurgery (13 papers)Pituitary (10 papers)Frontiers in Oncology (7 papers)Medicine (5 papers)
- Partner nations
- ChinaUnited StatesUnited Kingdom
In The Last Decade
Kan Deng
127 papers receiving 1.5k citations
Peers
Comparison fields: 5 of 127
- Endocrinology, Diabetes and Metabolism 804
- Genetics 176
- Health Informatics 10
- Cancer Research 99
- Epidemiology 182
Countries citing papers authored by Kan Deng
This map shows the geographic impact of Kan Deng'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 Kan Deng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kan Deng more than expected).
Fields of papers citing papers by Kan Deng
This network shows the impact of papers produced by Kan Deng. 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 Kan Deng. The network helps show where Kan Deng may publish in the future.
Co-authors
The 25 scholars most cited alongside Kan Deng, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 137 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 81 | |
| 2 | Efficient Locally Weighted Polynomial Regression Predictions | 1997 | 80 |
| 3 | Multiresolution instance-based learning | 1995 | 55 |
| 4 | 2017 | 53 | |
| 5 | 2016 | 49 | |
| 6 | 2002 | 46 | |
| 7 | 2010 | 41 | |
| 8 | 2017 | 40 | |
| 9 | 2020 | 38 | |
| 10 | 2015 | 33 | |
| 11 | 2017 | 33 | |
| 12 | 2016 | 33 | |
| 13 | 2013 | 32 | |
| 14 | 2018 | 31 | |
| 15 | 2020 | 29 | |
| 16 | Omega: on-line memory-based general purpose system classifier | 1999 | 29 |
| 17 | 2019 | 27 | |
| 18 | 2015 | 27 | |
| 19 | 2014 | 27 | |
| 20 | 2019 | 25 |
About Kan Deng
Kan Deng is a scholar working on Endocrinology, Diabetes and Metabolism, Molecular Biology, Surgery, Genetics and Epidemiology, having authored 137 papers that have together received 1.6k indexed citations. Recurring topics across this work include Pituitary Gland Disorders and Treatments (85 papers), Growth Hormone and Insulin-like Growth Factors (11 papers), Glioma Diagnosis and Treatment (9 papers), TGF-β signaling in diseases (4 papers), Neural Networks and Applications (3 papers), Adrenal and Paraganglionic Tumors (3 papers), Cancer-related molecular mechanisms research (3 papers) and Ferroptosis and cancer prognosis (3 papers). The work is most often cited by research in Endocrinology, Diabetes and Metabolism (804 citations), Genetics (176 citations), Health Informatics (10 citations), Cancer Research (99 citations) and Epidemiology (182 citations). Kan Deng has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Yong Yao, Renzhi Wang, Bing Xing, Andrew Moore, Wei Lian, Ming Feng, Xinjie Bao, Xiaopeng Guo, Lu Gao and Zihao Wang. Their work appears in journals such as Frontiers in Endocrinology, World Neurosurgery, Pituitary, Frontiers in Oncology and Medicine.
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.