Min-Gang Su
Impact in
- Spectroscopy top 10%
- Advanced Proteomics Techniques and Applications
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- Machine Learning in Bioinformatics
- Ubiquitin and proteasome pathways
- RNA and protein synthesis mechanisms
- Genomics and Phylogenetic Studies
- Bioinformatics and Genomic Networks
- Protein Structure and Dynamics
Papers in
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- RNA and protein synthesis mechanisms 5
- Machine Learning in Bioinformatics 3
- Protein Structure and Dynamics 2
- Bioinformatics and Genomic Networks 2
- Genomics and Phylogenetic Studies 1
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- Advanced Proteomics Techniques and Applications 3
- Co-authors
- Tzong-Yi Lee (11 shared papers)Kai‐Yao Huang (7 shared papers)Yu‐Ju Chen (4 shared papers)Yi‐Ju Chen (3 shared papers)Hui‐Ju Kao (3 shared papers)Neil Arvin Bretaña (3 shared papers)Jhih-Hua Jhong (2 shared papers)Cheng-Tsung Lu (5 shared papers)
- Journals
- Nucleic Acids Research (4 papers)BMC Systems Biology (2 papers)BMC Bioinformatics (2 papers)Database (1 paper)BioMed Research International (1 paper)
- Partner nations
- Taiwan
In The Last Decade
Min-Gang Su
12 papers receiving 603 citations
Peers
Comparison fields: 5 of 72
- Spectroscopy 123
- Molecular Biology 491
- Biochemistry 29
- Microbiology 19
- Oncology 63
Countries citing papers authored by Min-Gang Su
This map shows the geographic impact of Min-Gang Su'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 Min-Gang Su with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Min-Gang Su more than expected).
Fields of papers citing papers by Min-Gang Su
This network shows the impact of papers produced by Min-Gang Su. 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 Min-Gang Su. The network helps show where Min-Gang Su may publish in the future.
Co-authors
The 24 scholars most cited alongside Min-Gang Su, 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 | 2012 | 152 | |
| 2 | 2015 | 136 | |
| 3 | 2014 | 66 | |
| 4 | 2014 | 55 | |
| 5 | 2016 | 47 | |
| 6 | 2017 | 45 | |
| 7 | 2012 | 37 | |
| 8 | 2013 | 27 | |
| 9 | 2013 | 23 | |
| 10 | 2011 | 17 | |
| 11 | 2014 | 5 | |
| 12 | 2013 | 1 |
About Min-Gang Su
Min-Gang Su is a scholar working on Molecular Biology, Spectroscopy, Oncology, Ecology and Physiology, having authored 12 papers that have together received 611 indexed citations. Recurring topics across this work include RNA and protein synthesis mechanisms (5 papers), Advanced Proteomics Techniques and Applications (3 papers), Machine Learning in Bioinformatics (3 papers), Protein Structure and Dynamics (2 papers), Peptidase Inhibition and Analysis (2 papers), Bioinformatics and Genomic Networks (2 papers), Nitric Oxide and Endothelin Effects (1 paper) and Genomics and Phylogenetic Studies (1 paper). The work is most often cited by research in Spectroscopy (123 citations), Molecular Biology (491 citations), Biochemistry (29 citations), Microbiology (19 citations) and Oncology (63 citations). Min-Gang Su has collaborated with scholars based in Taiwan. Frequent co-authors include Tzong-Yi Lee, Kai‐Yao Huang, Yu‐Ju Chen, Yi‐Ju Chen, Hui‐Ju Kao, Neil Arvin Bretaña, Jhih-Hua Jhong, Cheng-Tsung Lu, Hsien‐Da Huang and Hsien-Da Huang. Their work appears in journals such as Nucleic Acids Research, BMC Systems Biology, BMC Bioinformatics, Database and BioMed Research International.
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.