Ming-Hsuang Cho
- Molecular Biology
- Computational Theory and Mathematics top 5%
- Small Animals top 5%
- Health, Toxicology and Mutagenesis top 10%
- Cancer Research
- Co-authors
- Ruili HuangMenghang XiaChristopher P. AustinJames IngleseNoel SouthallRaymond R. TiceKristine L. WittAjit Jadhav
- Topics
- Computational Drug Discovery Methods (4 papers)Carcinogens and Genotoxicity Assessment (2 papers)Receptor Mechanisms and Signaling (2 papers)
- Journals
- Proceedings of the National Academy of SciencesEnvironmental Health PerspectivesToxicological Sciences
- Partner nations
- United StatesTaiwan
In The Last Decade
Ming-Hsuang Cho
10 papers receiving 627 citations
Peers
Comparison fields: 5 of 105
- Molecular Biology 293
- Computational Theory and Mathematics 181
- Small Animals 101
- Health, Toxicology and Mutagenesis 101
- Cancer Research 74
Countries citing papers authored by Ming-Hsuang Cho
This map shows the geographic impact of Ming-Hsuang Cho'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 Ming-Hsuang Cho with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ming-Hsuang Cho more than expected).
Fields of papers citing papers by Ming-Hsuang Cho
This network shows the impact of papers produced by Ming-Hsuang Cho. 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 Ming-Hsuang Cho. The network helps show where Ming-Hsuang Cho may publish in the future.
Co-authorship network of co-authors of Ming-Hsuang Cho
This figure shows the co-authorship network connecting the top 25 collaborators of Ming-Hsuang Cho. A scholar is included among the top collaborators of Ming-Hsuang Cho 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 Ming-Hsuang Cho. Ming-Hsuang Cho is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 32 | |
| 2 | 166 | |
| 3 | 25 | |
| 4 | 50 | |
| 5 | 20 | |
| 6 | 69 | |
| 7 | 47 | |
| 8 | 196 | |
| 9 | 35 | |
| 10 | Pre-cultivation in suspension obtained chondrocyte-enriched cultures from articular-epiphyseal cartilage in fetal rats. | 1 |
About Ming-Hsuang Cho
Ming-Hsuang Cho is a scholar working on Computational Theory and Mathematics, Cancer Research and Rheumatology, having authored 10 papers that have together received 641 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (4 papers), Carcinogens and Genotoxicity Assessment (2 papers) and Receptor Mechanisms and Signaling (2 papers). The work is most often cited by research in Small Animals (101 citations), Computational Theory and Mathematics (181 citations) and Health, Toxicology and Mutagenesis (101 citations). Ming-Hsuang Cho has collaborated with scholars based in United States and Taiwan. Frequent co-authors include Ruili Huang, Menghang Xia, Christopher P. Austin, James Inglese, Noel Southall, Raymond R. Tice, Kristine L. Witt, Ajit Jadhav, Srilatha Sakamuru and Jennifer Fostel. Their work appears in journals such as Proceedings of the National Academy of Sciences, Environmental Health Perspectives and Toxicological Sciences.
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.