Lang Wu
Impact in
- Cancer Research top 5%
- Cancer, Lipids, and Metabolism
- Obstetrics and Gynecology top 5%
Papers in
- Co-authors
- Mingli Liu (2 shared papers)Jingjing Zhu (30 shared papers)Qingsen Ming (1 shared paper)Shuqiao Yao (1 shared paper)Qi‐Jun Wu (23 shared papers)Ting‐Ting Gong (20 shared papers)Changwei Li (6 shared papers)Larry J. Prokop (2 shared papers)
- Journals
- Cancer Epidemiology Biomarkers & Prevention (6 papers)International Journal of Cancer (6 papers)Cancer Research (5 papers)Scientific Reports (5 papers)Oncotarget (4 papers)
- Partner nations
- United StatesChinaUnited Kingdom
In The Last Decade
Lang Wu
127 papers receiving 3.9k citations
Peers
Comparison fields: 5 of 173
- Cancer Research 340
- Obstetrics and Gynecology 139
- Genetics 544
- Oncology 448
- Applied Psychology 66
Countries citing papers authored by Lang Wu
This map shows the geographic impact of Lang 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 Lang Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lang Wu more than expected).
Fields of papers citing papers by Lang Wu
This network shows the impact of papers produced by Lang 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 Lang Wu. The network helps show where Lang Wu may publish in the future.
Co-authors
The 25 scholars most cited alongside Lang 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
Showing the 20 most-cited of 140 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2015 | 283 | |
| 2 | 2015 | 277 | |
| 3 | 2020 | 231 | |
| 4 | 2018 | 162 | |
| 5 | 2015 | 123 | |
| 6 | 2015 | 105 | |
| 7 | 2015 | 95 | |
| 8 | 2015 | 90 | |
| 9 | 2014 | 89 | |
| 10 | 2015 | 80 | |
| 11 | 2018 | 79 | |
| 12 | 2014 | 77 | |
| 13 | 2014 | 74 | |
| 14 | 2015 | 72 | |
| 15 | 2014 | 63 | |
| 16 | 2014 | 60 | |
| 17 | 2019 | 60 | |
| 18 | 2015 | 57 | |
| 19 | 2018 | 55 | |
| 20 | 2024 | 55 |
About Lang Wu
Lang Wu is a scholar working on Oncology, Genetics, Cancer Research, Molecular Biology and Reproductive Medicine, having authored 140 papers that have together received 4.0k indexed citations. Recurring topics across this work include Genetic Associations and Epidemiology (26 papers), Pancreatic and Hepatic Oncology Research (13 papers), Epigenetics and DNA Methylation (13 papers), RNA modifications and cancer (11 papers), RNA Research and Splicing (9 papers), Bioinformatics and Genomic Networks (7 papers), Cancer-related molecular mechanisms research (6 papers) and Cancer, Lipids, and Metabolism (6 papers). The work is most often cited by research in Cancer Research (340 citations), Obstetrics and Gynecology (139 citations), Genetics (544 citations), Oncology (448 citations) and Applied Psychology (66 citations). Lang Wu has collaborated with scholars based in United States, China and United Kingdom. Frequent co-authors include Mingli Liu, Jingjing Zhu, Qingsen Ming, Shuqiao Yao, Qi‐Jun Wu, Ting‐Ting Gong, Changwei Li, Larry J. Prokop, Mohammad Hassan Murad and Chong Wu. Their work appears in journals such as Cancer Epidemiology Biomarkers & Prevention, International Journal of Cancer, Cancer Research, Scientific Reports and Oncotarget.
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.