Feng Gu
Impact in
- Cancer Research top 2%
- MicroRNA in disease regulation
- Cancer-related molecular mechanisms research
- Breast Cancer Treatment Studies
- Oncology top 2%
- Cancer Cells and Metastasis
- Cancer Immunotherapy and Biomarkers
Papers in
-
- Breast Cancer Treatment Studies 13
- Cancer, Hypoxia, and Metabolism 9
- MicroRNA in disease regulation 8
-
- Cell Adhesion Molecules Research 12
- Journals
- Breast Cancer Research and Treatment (7 papers)Oncotarget (6 papers)Cell Death and Disease (6 papers)PLoS ONE (4 papers)Scientific Reports (3 papers)
- Partner nations
- ChinaUnited StatesJapan
In The Last Decade
Feng Gu
140 papers receiving 3.2k citations
Peers
Comparison fields: 5 of 120
- Cancer Research 989
- Oncology 1.0k
- Developmental Neuroscience 117
- Molecular Biology 1.6k
- Immunology 494
Countries citing papers authored by Feng Gu
This map shows the geographic impact of Feng Gu'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 Feng Gu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Feng Gu more than expected).
Fields of papers citing papers by Feng Gu
This network shows the impact of papers produced by Feng Gu. 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 Feng Gu. The network helps show where Feng Gu may publish in the future.
Co-authors
The 25 scholars most cited alongside Feng Gu, 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 | 16 | |
| 3 | 2022 | 7 | |
| 4 | 2021 | 46 | |
| 5 | 2021 | 4 | |
| 6 | 2020 | 1 | |
| 7 | 2015 | 28 | |
| 8 | 2013 | 2 | |
| 9 | 2013 | 23 | |
| 10 | 2013 | 39 | |
| 11 | [Cytoplasmic expression of aquaporin-1 in breast cancer cells and its relationship with clinicopathological characteristics and prognosis]. | 2013 | 3 |
| 12 | 2012 | 1 | |
| 13 | 2012 | 34 | |
| 14 | 2012 | 22 | |
| 15 | Research progression of mechanisms in glioma chemoresistance | 2011 | 1 |
| 16 | Co-culture with microglia promotes neural stem cells differentiation into astrocytes. | 2011 | 9 |
| 17 | 2009 | 72 | |
| 18 | [Significance of expression of stromal cell derived factor 1 and CXCR4 in invasive breast cancer]. | 2008 | 3 |
| 19 | 2002 | 19 | |
| 20 | 1995 | 7 |
About Feng Gu
Feng Gu is a scholar working on Cancer Research, Immunology and Allergy, Oncology, Cell Biology and Pathology and Forensic Medicine, having authored 146 papers that have together received 3.2k indexed citations. Recurring topics across this work include Breast Cancer Treatment Studies (13 papers), Breast Lesions and Carcinomas (13 papers), Cell Adhesion Molecules Research (12 papers), Cancer Cells and Metastasis (11 papers), Cancer, Hypoxia, and Metabolism (9 papers), HER2/EGFR in Cancer Research (8 papers), RNA Research and Splicing (8 papers) and MicroRNA in disease regulation (8 papers). The work is most often cited by research in Cancer Research (989 citations), Oncology (1.0k citations), Developmental Neuroscience (117 citations), Molecular Biology (1.6k citations) and Immunology (494 citations). Feng Gu has collaborated with scholars based in China, United States and Japan. Frequent co-authors include Li Fu, Ronggang Lang, Yongjie Ma, Xinmin Zhang, Xiaojing Guo, Li Fu, Yu Fan, Li Fu, Fangfang Liu and Gordon A. Pringle. Their work appears in journals such as Breast Cancer Research and Treatment, Oncotarget, Cell Death and Disease, PLoS ONE and Scientific Reports.
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.