Chenzhe Feng

459 citations
20 papers · 279 indexed · h-index 10
Topics
Pituitary Gland Disorders and Treatments (10 papers)Growth Hormone and Insulin-like Growth Factors (6 papers)Radiomics and Machine Learning in Medical Imaging (3 papers)

In The Last Decade

Chenzhe Feng

20 papers receiving 276 citations

Peers

Chenzhe Feng
Comparison fields: 5 of 76
  • Molecular Biology 75
  • Endocrinology, Diabetes and Metabolism 66
  • Pulmonary and Respiratory Medicine 63
  • Epidemiology 60
  • Cancer Research 55
Replace H.K. Cheow with:
H.K. Cheow United Kingdom
Aeron Small United States
Zhengyu Hu China
Melissa McGettigan United States
Noriaki Kasayuki Japan
Chie Yoshimura Japan
Alex E. Henney United Kingdom
Anna Castiglione Italy
Chenzhe Feng relative to H.K. Cheow United Kingdom H.K. Cheow's profile →
Citations per field
00.5×1.5×2.4×
H.K. Cheow · 1×
Citations per year

Countries citing papers authored by Chenzhe Feng

Since Specialization
Citations

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

Fields of papers citing papers by Chenzhe Feng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chenzhe Feng

This figure shows the co-authorship network connecting the top 25 collaborators of Chenzhe Feng. A scholar is included among the top collaborators of Chenzhe Feng 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 Chenzhe Feng. Chenzhe Feng 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
#WorkIndexed citations
1 7
2 1
3 10
4 6
5 7
6 9
7 22
8
Comprehensive identification of a two-genesignature as a novel potential prognostic model for patients with medulloblastoma.
3
9 3
10 25
11 17
12 7
13 3
14 5
15 16
16 79
17 27
18 15
19 9
20 8

About Chenzhe Feng

Chenzhe Feng is a scholar working on Health Informatics, Endocrinology, Diabetes and Metabolism and Genetics, having authored 20 papers that have together received 279 indexed citations. Recurring topics across this work include Pituitary Gland Disorders and Treatments (10 papers), Growth Hormone and Insulin-like Growth Factors (6 papers) and Radiomics and Machine Learning in Medical Imaging (3 papers). The work is most often cited by research in Health Informatics (11 citations), Genetics (48 citations) and Endocrinology, Diabetes and Metabolism (66 citations). Chenzhe Feng has collaborated with scholars based in China, United States and Germany. Frequent co-authors include Xiaopeng Guo, Lu Gao, Bing Xing, Zihao Wang, Wei Lian, Kan Deng, Zihao Wang, Yijun Wu, Yeqian Feng and Ming Feng. Their work appears in journals such as Frontiers in Oncology, Frontiers in Endocrinology and Aging.

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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