Te-Chia Wu
Impact in
- Immunology top 10%
- Immune Cell Function and Interaction
- Immunotherapy and Immune Responses
- T-cell and B-cell Immunology
- Immune cells in cancer
- Immunology and Allergy top 10%
Papers in
-
- Immunotherapy and Immune Responses 6
- Immune Cell Function and Interaction 3
- Immune cells in cancer 2
- Oncology 6
- Cancer Immunotherapy and Biomarkers 4
- CAR-T cell therapy research 2
- Co-authors
- Karolina Palucka (12 shared papers)Jacques Banchereau (8 shared papers)Florentina Marches (8 shared papers)Alexander Pedroza‐González (3 shared papers)Kangling Xu (4 shared papers)Yong‐Jun Liu (2 shared papers)Jan Martínek (9 shared papers)Joshy George (4 shared papers)
- Journals
- The Journal of Experimental Medicine (3 papers)The Journal of Immunology (2 papers)Journal of Virology (1 paper)International review of cell and molecular biology (1 paper)Methods in enzymology on CD-ROM/Methods in enzymology (1 paper)
- Partner nations
- United StatesJapanTaiwan
In The Last Decade
Te-Chia Wu
13 papers receiving 531 citations
Peers
Comparison fields: 5 of 64
- Immunology 283
- Immunology and Allergy 41
- Dermatology 57
- Oncology 151
- Biological Psychiatry 10
Countries citing papers authored by Te-Chia Wu
This map shows the geographic impact of Te-Chia 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 Te-Chia Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Te-Chia Wu more than expected).
Fields of papers citing papers by Te-Chia Wu
This network shows the impact of papers produced by Te-Chia 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 Te-Chia Wu. The network helps show where Te-Chia Wu may publish in the future.
Co-authors
The 25 scholars most cited alongside Te-Chia Wu, 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 | 2011 | 193 | |
| 2 | 2017 | 147 | |
| 3 | 2014 | 66 | |
| 4 | 2007 | 49 | |
| 5 | 2019 | 35 | |
| 6 | 2022 | 19 | |
| 7 | 2022 | 9 | |
| 8 | 2023 | 7 | |
| 9 | 2021 | 6 | |
| 10 | 2019 | 4 | |
| 11 | 2022 | 1 | |
| 12 | 2011 | 1 | |
| 13 | 2022 | 1 | |
| 14 | 2016 | 0 |
About Te-Chia Wu
Te-Chia Wu is a scholar working on Immunology, Oncology, Infectious Diseases, Molecular Biology and Dermatology, having authored 14 papers that have together received 538 indexed citations. Recurring topics across this work include Immunotherapy and Immune Responses (6 papers), Cancer Immunotherapy and Biomarkers (4 papers), Immune Cell Function and Interaction (3 papers), Dermatology and Skin Diseases (2 papers), Viral gastroenteritis research and epidemiology (2 papers), Immune cells in cancer (2 papers), CAR-T cell therapy research (2 papers) and Radiomics and Machine Learning in Medical Imaging (1 paper). The work is most often cited by research in Immunology (283 citations), Immunology and Allergy (41 citations), Dermatology (57 citations), Oncology (151 citations) and Biological Psychiatry (10 citations). Te-Chia Wu has collaborated with scholars based in United States, Japan and Taiwan. Frequent co-authors include Karolina Palucka, Jacques Banchereau, Florentina Marches, Alexander Pedroza‐González, Kangling Xu, Yong‐Jun Liu, Jan Martínek, Joshy George, Duygu Ucar and Aslı Uyar. Their work appears in journals such as The Journal of Experimental Medicine, The Journal of Immunology, Journal of Virology, International review of cell and molecular biology and Methods in enzymology on CD-ROM/Methods in enzymology.
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.