Computational Feature Extraction
Computational Feature Extraction is a systematic method within single-cell metabolomics that identifies and isolates quantifiable molecular signals from complex raw data through algorithmic processing, distinguishing itself by enabling high-resolution metabolic characterization at the individual cell level.
268
Publications
14.9K
Citations
1.1K
Authors
467
Institutions
Publications per year
2017–2026
129
Parents
268
Weisfeiler-lehman graph kernels
Max Planck Digital Library · 2010 · 1.6K citations · Full text
Predicting Network Activity from High Throughput Metabolomics
Shuzhao Li, Youngja Park, Sai Duraisingham et al. · PLoS Computational Biology · 2013 · 970 citations · Full text
MPED: A Multi-Modal Physiological Emotion Database for Discrete Emotion Recognition
Tengfei Song, Wenming Zheng, Lu Cheng et al. · IEEE Access · 2019 · 325 citations · Full text
1–5 of 268
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University of Electronic Science and Technology of China, China
3 publications · 205 citations
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467

University of Electronic Science and Technology of China
Chengdu, China
6 h-index
24 publications
2.3K citations

Chinese Academy of Sciences
Beijing, China
5 h-index
8 publications
950 citations

Southeast University
Nanjing, China
4 h-index
13 publications
1.9K citations
Nanyang Technological University
Singapore, Singapore
4 h-index
9 publications
540 citations

Shanghai Jiao Tong University
Shanghai, China
4 h-index
20 publications
1.1K citations
Xiamen University
Xiamen, China
4 h-index
13 publications
711 citations

University of British Columbia
Vancouver, Canada
3 h-index
11 publications
449 citations

University of Calgary
Calgary, Canada
3 h-index
2 publications
56 citations
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8 publications · 677 citations
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7 publications · 275 citations
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Repository · 4 publications · 58 citations
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4 publications · 191 citations
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4 publications · 155 citations
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