Win Topatana
Impact in
- Health Informatics top 5%
- Hepatology top 10%
- Hepatocellular Carcinoma Treatment and Prognosis
Papers in
-
- Hepatocellular Carcinoma Treatment and Prognosis 5
- Oncology 7
- PARP inhibition in cancer therapy 2
- Co-authors
- Mingyu Chen (27 shared papers)Sarun Juengpanich (25 shared papers)Jiasheng Cao (25 shared papers)Xiujun Cai (21 shared papers)Shijie Li (24 shared papers)Jiahao Hu (24 shared papers)Bin Zhang (10 shared papers)Jiliang Shen (10 shared papers)
- Journals
- Frontiers in Oncology (4 papers)Advanced Science (2 papers)Journal of Hematology & Oncology (2 papers)World Journal of Gastroenterology (2 papers)Nature Communications (2 papers)
- Partner nations
- ChinaThailandUnited States
In The Last Decade
Win Topatana
34 papers receiving 1.3k citations
Win Topatana's Hit Papers
Peers
Comparison fields: 5 of 110
- Health Informatics 35
- Hepatology 119
- Cancer Research 179
- Oncology 315
- Radiology, Nuclear Medicine and Imaging 161
Countries citing papers authored by Win Topatana
This map shows the geographic impact of Win Topatana'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 Win Topatana with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Win Topatana more than expected).
Fields of papers citing papers by Win Topatana
This network shows the impact of papers produced by Win Topatana. 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 Win Topatana. The network helps show where Win Topatana may publish in the future.
Co-authors
The 25 scholars most cited alongside Win Topatana, 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 35 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Targeting mutant p53 for cancer therapy: direct and indirect strategies Hit paper breakdown → | 2021 | 386 |
| 2 | 2020 | 192 | |
| 3 | 2020 | 139 | |
| 4 | 2021 | 98 | |
| 5 | 2020 | 88 | |
| 6 | 2023 | 82 | |
| 7 | 2020 | 37 | |
| 8 | 2021 | 33 | |
| 9 | 2022 | 28 | |
| 10 | 2022 | 26 | |
| 11 | 2024 | 24 | |
| 12 | 2020 | 23 | |
| 13 | 2021 | 21 | |
| 14 | 2024 | 17 | |
| 15 | 2024 | 16 | |
| 16 | 2021 | 16 | |
| 17 | 2020 | 13 | |
| 18 | 2020 | 13 | |
| 19 | 2022 | 13 | |
| 20 | 2021 | 13 |
About Win Topatana
Win Topatana is a scholar working on Hepatology, Oncology, Surgery, Cancer Research and Molecular Biology, having authored 35 papers that have together received 1.3k indexed citations. Recurring topics across this work include Cholangiocarcinoma and Gallbladder Cancer Studies (9 papers), Hepatocellular Carcinoma Treatment and Prognosis (5 papers), DNA Repair Mechanisms (3 papers), Radiomics and Machine Learning in Medical Imaging (3 papers), Nanoplatforms for cancer theranostics (3 papers), Advanced Nanomaterials in Catalysis (2 papers), Metabolism, Diabetes, and Cancer (2 papers) and PARP inhibition in cancer therapy (2 papers). The work is most often cited by research in Health Informatics (35 citations), Hepatology (119 citations), Cancer Research (179 citations), Oncology (315 citations) and Radiology, Nuclear Medicine and Imaging (161 citations). Win Topatana has collaborated with scholars based in China, Thailand and United States. Frequent co-authors include Mingyu Chen, Sarun Juengpanich, Jiasheng Cao, Xiujun Cai, Shijie Li, Jiahao Hu, Bin Zhang, Jiliang Shen, Liuxin Cai and Hong Yu. Their work appears in journals such as Frontiers in Oncology, Advanced Science, Journal of Hematology & Oncology, World Journal of Gastroenterology and Nature Communications.
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.