Semi-supervised Binning
Semi-Supervised Binning is a metagenomic binning approach that belongs to the class of computational methods utilizing both labeled reference genomes and unlabeled sequence data to refine taxonomic or functional genome aggregations, distinguishing itself from unsupervised and fully supervised techniques by integrating partial prior knowledge into the clustering process.
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10
Institutions
Publications per year
2017–2026
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SolidBin: improving metagenome binning with semi-supervised normalized cut
Ziye Wang, Zhengyang Wang, Yang Young Lu et al. · Bioinformatics · 2019 · 89 citations · Full text
Binning sequences using very sparse labels within a metagenome
Chon‐Kit Kenneth Chan, Arthur Hsu, Saman Halgamuge et al. · BMC Bioinformatics · 2008 · 88 citations · Full text
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Shanghai Institute for Science of Science
Xuhui, China
3 h-index
11 publications
1.7K citations
Fudan University
Shanghai, China
3 h-index
11 publications
1.7K citations
Shanghai Center for Brain Science and Brain-Inspired Technology
Shanghai, China
3 h-index
11 publications
1.7K citations
University of Southern California
Los Angeles, United States
1 h-index
2 publications
178 citations

University of Hong Kong
Pok Fu Lam, Hong Kong
1 h-index
5 publications
270 citations

Shanghai Innovative Research Center of Traditional Chinese Medicine
Shanghai, China
1 h-index
1 publications
188 citations

Southeast University
Nanjing, China
1 h-index
1 publications
54 citations

The University of Melbourne
Melbourne, Australia
1 h-index
3 publications
264 citations
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2 publications · 142 citations
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2 publications · 277 citations
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1 publications · 187 citations
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Repository · 1 publications · 10 citations
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