M.C.K. Yang

23 papers receiving 594 citations

Peers

M.C.K. Yang
Comparison fields: 5 of 111
  • Software 35
  • Cognitive Neuroscience 144
  • Statistics and Probability 38
  • Periodontics 18
  • Molecular Biology 241
Replace Konrad Rawlik with:
Konrad Rawlik United Kingdom
Vanathi Gopalakrishnan United States
Yiming Hu United States
Stella Lee United States
Ling Zhao China
Qiang Guan China
Xiao Lei Zhang China
Alain Giron France
Yanbo Xu China
M.C.K. Yang relative to Konrad Rawlik United Kingdom Konrad Rawlik's profile →
Citations per field
00.5×10×15×17.5×
Konrad Rawlik · 1×
Citations per year

Countries citing papers authored by M.C.K. Yang

Since Specialization
Citations

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

Fields of papers citing papers by M.C.K. Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by M.C.K. Yang. 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 M.C.K. Yang. The network helps show where M.C.K. Yang may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 27 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1981182
2 200191
3 197762
4 201061
5 199740
6 199536
7 200330
8 200024
9 200421
10 197920
11 201019
12 200918
13 19769
14 20248
15 19947
16 20115
17 19893
18 20022
19 20251
20 20251

About M.C.K. Yang

M.C.K. Yang is a scholar working on Molecular Biology, Computer Vision and Pattern Recognition, Cognitive Neuroscience, Information Systems and Artificial Intelligence, having authored 27 papers that have together received 643 indexed citations. Recurring topics across this work include EEG and Brain-Computer Interfaces (4 papers), Gene expression and cancer classification (3 papers), Spectroscopy and Chemometric Analyses (2 papers), Software Engineering Research (2 papers), Smart Agriculture and AI (2 papers), Software Testing and Debugging Techniques (2 papers), Software Reliability and Analysis Research (2 papers) and Advanced Image Fusion Techniques (2 papers). The work is most often cited by research in Software (35 citations), Cognitive Neuroscience (144 citations), Statistics and Probability (38 citations), Periodontics (18 citations) and Molecular Biology (241 citations). M.C.K. Yang has collaborated with scholars based in United States, China and Taiwan. Frequent co-authors include James C. Bonner, Thomas D. Sargent, J. R. Smith, Ismet Karacan, Jin‐Xiong She, Richard McIndoe, James J. Yang, Anne Chao, Sarah Eckenrode and Qingguo Ruan. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, International Journal of Biological Macromolecules, Physiological Genomics, Electroencephalography and Clinical Neurophysiology and Clinical Neurophysiology.

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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