Min-Gang Su
Impact in
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- Machine Learning in Bioinformatics
- Ubiquitin and proteasome pathways
- Genomics and Phylogenetic Studies
- RNA and protein synthesis mechanisms
- Bioinformatics and Genomic Networks
- Protein Structure and Dynamics
- Spectroscopy top 10%
- Advanced Proteomics Techniques and Applications
Papers in
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- Protein Structure and Dynamics 5
- Machine Learning in Bioinformatics 4
- RNA and protein synthesis mechanisms 4
- Bioinformatics and Genomic Networks 2
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- Advanced Proteomics Techniques and Applications 3
- Co-authors
- Tzong-Yi Lee (11 shared papers)Kai‐Yao Huang (7 shared papers)Hui‐Ju Kao (3 shared papers)Jhih-Hua Jhong (2 shared papers)Cheng-Tsung Lu (5 shared papers)Hsien‐Da Huang (2 shared papers)Shun‐Long Weng (2 shared papers)Yu‐Ju Chen (3 shared papers)
- Journals
- Nucleic Acids Research (3 papers)BMC Systems Biology (2 papers)BMC Bioinformatics (2 papers)BioMed Research International (1 paper)PLoS ONE (1 paper)
- Partner nations
- Taiwan
In The Last Decade
Min-Gang Su
11 papers receiving 443 citations
Peers
Comparison fields: 5 of 70
- Molecular Biology 384
- Spectroscopy 91
- Microbiology 24
- Biochemistry 28
- Oncology 64
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 21 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 | 2015 | 135 | |
| 2 | 2014 | 66 | |
| 3 | 2014 | 51 | |
| 4 | 2016 | 46 | |
| 5 | 2017 | 43 | |
| 6 | 2012 | 37 | |
| 7 | 2013 | 27 | |
| 8 | 2013 | 23 | |
| 9 | 2011 | 17 | |
| 10 | 2014 | 4 | |
| 11 | 2013 | 1 |
About Min-Gang Su
Min-Gang Su is a scholar working on Molecular Biology, Spectroscopy, Oncology, Materials Chemistry and Pharmacology, having authored 11 papers that have together received 450 indexed citations. Recurring topics across this work include Protein Structure and Dynamics (5 papers), Machine Learning in Bioinformatics (4 papers), RNA and protein synthesis mechanisms (4 papers), Advanced Proteomics Techniques and Applications (3 papers), Bioinformatics and Genomic Networks (2 papers), Enzyme Structure and Function (2 papers), Peptidase Inhibition and Analysis (2 papers) and Computational Drug Discovery Methods (1 paper). The work is most often cited by research in Molecular Biology (384 citations), Spectroscopy (91 citations), Microbiology (24 citations), Biochemistry (28 citations) and Oncology (64 citations). Min-Gang Su has collaborated with scholars based in Taiwan. Frequent co-authors include Tzong-Yi Lee, Kai‐Yao Huang, Hui‐Ju Kao, Jhih-Hua Jhong, Cheng-Tsung Lu, Hsien‐Da Huang, Shun‐Long Weng, Yu‐Ju Chen, Yi‐Ju Chen and Neil Arvin Bretaña. Their work appears in journals such as Nucleic Acids Research, BMC Systems Biology, BMC Bioinformatics, BioMed Research International 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.