Zhi-Fen Wu
- Molecular Biology top 10%
- Oncology top 5%
- Cancer Research top 5%
- Cell Biology top 10%
- Genetics
- Co-authors
- Sofía D. MerajverLuc Van KaerCelina G. KleerYanhong ZhangLiwen BaoTim ZacharewskiMark A. RubinQuintin Pan
- Topics
- Cell Adhesion Molecules Research (5 papers)Wnt/β-catenin signaling in development and cancer (5 papers)Protein Kinase Regulation and GTPase Signaling (4 papers)
- Partner nations
- United StatesCanada
In The Last Decade
Zhi-Fen Wu
14 papers receiving 1.3k citations
Peers
Comparison fields: 5 of 74
- Molecular Biology 867
- Oncology 566
- Cancer Research 367
- Cell Biology 187
- Genetics 165
Countries citing papers authored by Zhi-Fen Wu
This map shows the geographic impact of Zhi-Fen Wu'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 Zhi-Fen Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Zhi-Fen Wu more than expected).
Fields of papers citing papers by Zhi-Fen Wu
This network shows the impact of papers produced by Zhi-Fen Wu. 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 Zhi-Fen Wu. The network helps show where Zhi-Fen Wu may publish in the future.
Co-authorship network of co-authors of Zhi-Fen Wu
This figure shows the co-authorship network connecting the top 25 collaborators of Zhi-Fen Wu. A scholar is included among the top collaborators of Zhi-Fen Wu 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 Zhi-Fen Wu. Zhi-Fen Wu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 55 | |
| 3 | 14 | |
| 4 | 90 | |
| 5 | 56 | |
| 6 | 75 | |
| 7 | 119 | |
| 8 | 169 | |
| 9 | 121 | |
| 10 | RhoC GTPase, a novel transforming oncogene for human mammary epithelial cells that partially recapitulates the inflammatory breast cancer phenotype. | 259 |
| 11 | A novel putative low-affinity insulin-like growth factor-binding protein, LIBC (lost in inflammatory breast cancer), and RhoC GTPase correlate with the inflammatory breast cancer phenotype. | 208 |
| 12 | 9 | |
| 13 | Assessment of the alleged estrogen receptor-mediated activity of phthalate esters | 6 |
| 14 | Antiestrogenic effect of 2,3,7,8-tetrachlorodibenzo-p-dioxin on 17 beta-estradiol-induced pS2 expression. | 108 |
About Zhi-Fen Wu
Zhi-Fen Wu is a scholar working on Immunology and Allergy, Molecular Biology and Health, Toxicology and Mutagenesis, having authored 14 papers that have together received 1.3k indexed citations. Recurring topics across this work include Cell Adhesion Molecules Research (5 papers), Wnt/β-catenin signaling in development and cancer (5 papers) and Protein Kinase Regulation and GTPase Signaling (4 papers). The work is most often cited by research in Cancer Research (367 citations), Oncology (566 citations) and Immunology and Allergy (90 citations). Zhi-Fen Wu has collaborated with scholars based in United States and Canada. Frequent co-authors include Sofía D. Merajver, Luc Van Kaer, Celina G. Kleer, Yanhong Zhang, Liwen Bao, Tim Zacharewski, Mark A. Rubin, Quintin Pan, Mei Wu and Gary W. Gallagher. Their work appears in journals such as Cancer, Oncogene and American Journal Of Pathology.
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.