Mu Su

1.4k citations
47 papers · 1.0k · h-index 14

Impact in

Papers in

    • RNA Research and Splicing 5
    • Genomics and Chromatin Dynamics 5
    • Epigenetics and DNA Methylation 4
    • RNA regulation and disease 3
    • Neural Networks and Applications 5
    • Advanced Clustering Algorithms Research 3

Mu Su

44 papers receiving 994 citations

Peers

Mu Su
Comparison fields: 5 of 103
  • Developmental Neuroscience 105
  • Neurology 124
  • Cellular and Molecular Neuroscience 199
  • Molecular Biology 457
  • Computer Networks and Communications 131
Replace Young Seek Lee with:
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Xuan Wang China
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Mu Su relative to Young Seek Lee South Korea Young Seek Lee's profile →
Citations per field
00.5×1.5×1.8×
Young Seek Lee · 1×
Citations per year

Countries citing papers authored by Mu Su

Since Specialization
Citations

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

Fields of papers citing papers by Mu Su

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mu 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 Mu Su. The network helps show where Mu Su may publish in the future.

Co-authors

The 25 scholars most cited alongside Mu Su, 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 Mu Su Line = papers co-authored together Mu Su links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 47 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2008285
2 2014129
3 200697
4 200494
5 200657
6 200643
7
Symmetry as a new measure for cluster validity
200241
8 202131
9 201525
10 202025
11 201720
12 200220
13
A new cluster validity measure for clusters with different densities
200315
14 201915
15 201211
16 202011
17 201910
18 20219
19 20199
20 19998

About Mu Su

Mu Su is a scholar working on Molecular Biology, Artificial Intelligence, Computer Vision and Pattern Recognition, Control and Systems Engineering and Pulmonary and Respiratory Medicine, having authored 47 papers that have together received 1.0k indexed citations. Recurring topics across this work include Face and Expression Recognition (5 papers), RNA Research and Splicing (5 papers), Neural Networks and Applications (5 papers), Genomics and Chromatin Dynamics (5 papers), Epigenetics and DNA Methylation (4 papers), RNA regulation and disease (3 papers), Cancer-related molecular mechanisms research (3 papers) and Advanced Clustering Algorithms Research (3 papers). The work is most often cited by research in Developmental Neuroscience (105 citations), Neurology (124 citations), Cellular and Molecular Neuroscience (199 citations), Molecular Biology (457 citations) and Computer Networks and Communications (131 citations). Mu Su has collaborated with scholars based in China, Taiwan and United States. Frequent co-authors include Michael Brenner, Albee Messing, Youngjin Lee, Eugene Lai, Zhemin Duan, Zehua Guo, Yang Xu, H. Jonathan Chao, Huimin Hu and Alessandra d’Azzo. Their work appears in journals such as Cell Death and Disease, Epigenomics, Glia, Frontiers in Genetics and Scientific Reports.

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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