Ching-Tai Chen

556 citations
6 papers · 137 · h-index 5

Impact in

    • Machine Learning in Bioinformatics
    • Protein Structure and Dynamics
    • RNA and protein synthesis mechanisms
    • Genomics and Phylogenetic Studies
    • Glycosylation and Glycoproteins Research
    • vaccines and immunoinformatics approaches
    • Monoclonal and Polyclonal Antibodies Research

Papers in

    • Machine Learning in Bioinformatics 3
    • Protein Structure and Dynamics 3
    • Glycosylation and Glycoproteins Research 2
    • Genomics and Phylogenetic Studies 2
    • Metabolomics and Mass Spectrometry Studies 1
    • RNA and protein synthesis mechanisms 1

Ching-Tai Chen

6 papers receiving 135 citations

Peers

Ching-Tai Chen
Comparison fields: 5 of 38
  • Molecular Biology 121
  • Radiology, Nuclear Medicine and Imaging 35
  • Computational Theory and Mathematics 22
  • Virology 5
  • Spectroscopy 15
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Julia Michelotti United States
Yasaman Karami France
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João M. Dias Portugal
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Citations per field
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Citations per year

Countries citing papers authored by Ching-Tai Chen

Since Specialization
Citations

This map shows the geographic impact of Ching-Tai Chen'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 Ching-Tai Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ching-Tai Chen more than expected).

Fields of papers citing papers by Ching-Tai Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ching-Tai Chen. 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 Ching-Tai Chen. The network helps show where Ching-Tai Chen may publish in the future.

Co-authors

The 15 scholars most cited alongside Ching-Tai Chen, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Ching-Tai Chen Line = papers co-authored together Ching-Tai Chen links everyone, so they are left out of the graph.

All Works

6 of 6 papers shown
#Work
1 200944
2 201243
3 201225
4 201615
5 20169
6 20051

About Ching-Tai Chen

Ching-Tai Chen is a scholar working on Molecular Biology, Organic Chemistry, Ecology, Computational Theory and Mathematics and Radiology, Nuclear Medicine and Imaging, having authored 6 papers that have together received 137 indexed citations. Recurring topics across this work include Machine Learning in Bioinformatics (3 papers), Protein Structure and Dynamics (3 papers), Glycosylation and Glycoproteins Research (2 papers), Genomics and Phylogenetic Studies (2 papers), Computational Drug Discovery Methods (1 paper), Metabolomics and Mass Spectrometry Studies (1 paper), RNA and protein synthesis mechanisms (1 paper) and Monoclonal and Polyclonal Antibodies Research (1 paper). The work is most often cited by research in Molecular Biology (121 citations), Radiology, Nuclear Medicine and Imaging (35 citations), Computational Theory and Mathematics (22 citations), Virology (5 citations) and Spectroscopy (15 citations). Ching-Tai Chen has collaborated with scholars based in Taiwan. Frequent co-authors include Wen-Lian Hsu, Ting-Yi Sung, Shinn‐Ying Ho, Hsin-Nan Lin, Hung‐Pin Peng, Keng‐Chang Tsai, An‐Suei Yang, Kai-Fa Huang, Jhih-Wei Jian and Yu‐Ching Lee. Their work appears in journals such as PLoS ONE, Nucleic Acids Research and BMC Bioinformatics.

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.

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