Hsi Chang
Impact in
- Genetics top 10%
- Mesenchymal stem cell research
-
- Pluripotent Stem Cells Research
- Muscle Physiology and Disorders
- CRISPR and Genetic Engineering
Papers in
-
- Muscle Physiology and Disorders 9
- Pluripotent Stem Cells Research 5
- Genetics 11
- Glioma Diagnosis and Treatment 5
- Mesenchymal stem cell research 4
- Co-authors
- Tatsutoshi Nakahata (10 shared papers)Katsutsugu Umeda (5 shared papers)Toshio Heike (4 shared papers)Akira Niwa (5 shared papers)Tomonari Awaya (2 shared papers)So‐ichiro Fukada (3 shared papers)Toru Iwasa (2 shared papers)Hiroshi Yamamoto (2 shared papers)
- Journals
- Chinese Medicine (2 papers)Epilepsy Research (2 papers)International Journal of Molecular Sciences (2 papers)The FASEB Journal (2 papers)PLoS ONE (2 papers)
- Partner nations
- TaiwanJapanUnited States
In The Last Decade
Hsi Chang
32 papers receiving 516 citations
Peers
Comparison fields: 5 of 81
- Genetics 101
- Molecular Biology 310
- Surgery 173
- Radiology, Nuclear Medicine and Imaging 68
- Hematology 32
Countries citing papers authored by Hsi Chang
This map shows the geographic impact of Hsi Chang'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 Hsi Chang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hsi Chang more than expected).
Fields of papers citing papers by Hsi Chang
This network shows the impact of papers produced by Hsi Chang. 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 Hsi Chang. The network helps show where Hsi Chang may publish in the future.
Co-authors
The 25 scholars most cited alongside Hsi Chang, 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 39 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2010 | 122 | |
| 2 | 2009 | 64 | |
| 3 | 2012 | 62 | |
| 4 | 2009 | 31 | |
| 5 | 2011 | 25 | |
| 6 | 2022 | 24 | |
| 7 | 2020 | 19 | |
| 8 | 2009 | 18 | |
| 9 | 2020 | 16 | |
| 10 | 2018 | 15 | |
| 11 | 2019 | 14 | |
| 12 | 2022 | 12 | |
| 13 | 2022 | 12 | |
| 14 | 2004 | 11 | |
| 15 | 2005 | 11 | |
| 16 | 2003 | 10 | |
| 17 | 2020 | 7 | |
| 18 | 2022 | 7 | |
| 19 | 2020 | 5 | |
| 20 | 2018 | 5 |
About Hsi Chang
Hsi Chang is a scholar working on Molecular Biology, Genetics, Radiology, Nuclear Medicine and Imaging, Psychiatry and Mental health and Surgery, having authored 39 papers that have together received 522 indexed citations. Recurring topics across this work include Muscle Physiology and Disorders (9 papers), Glioma Diagnosis and Treatment (5 papers), Laser Applications in Dentistry and Medicine (5 papers), Pluripotent Stem Cells Research (5 papers), Mesenchymal stem cell research (4 papers), Tissue Engineering and Regenerative Medicine (3 papers), Epilepsy research and treatment (3 papers) and Neonatal and fetal brain pathology (2 papers). The work is most often cited by research in Genetics (101 citations), Molecular Biology (310 citations), Surgery (173 citations), Radiology, Nuclear Medicine and Imaging (68 citations) and Hematology (32 citations). Hsi Chang has collaborated with scholars based in Taiwan, Japan and United States. Frequent co-authors include Tatsutoshi Nakahata, Katsutsugu Umeda, Toshio Heike, Akira Niwa, Tomonari Awaya, So‐ichiro Fukada, Toru Iwasa, Hiroshi Yamamoto, Shinya Yamanaka and Momoko Yoshimoto. Their work appears in journals such as Chinese Medicine, Epilepsy Research, International Journal of Molecular Sciences, The FASEB Journal and PLoS ONE.
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.