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.

5

Publications

606

Citations

19

Authors

10

Institutions

Publications per year

2017–2026

3

Authors

19

  1. XZ
    Xing‐Ming Zhao

    Fudan University, China

    2 publications · 375 citations

    2

    h-index

  2. LP
    Luís Pedro Coelho

    European Molecular Biology Laboratory, Germany

    2 publications · 375 citations

    2

    h-index

  3. SP
    Shaojun Pan

    Shanghai Jiao Tong University, China

    2 publications · 375 citations

    2

    h-index

  4. CK
    Chon‐Kit Kenneth Chan

    The University of Western Australia, Australia

    1 publications · 88 citations

    1

    h-index

  5. SZ
    Shanfeng Zhu

    Fudan University, China

    1 publications · 89 citations

    1

    h-index

1–5 of 19

Venues

  1. BMC Bioinformatics

    2 publications · 142 citations

    2

    h-index

  2. Bioinformatics

    2 publications · 277 citations

    2

    h-index

  3. Nature Communications

    1 publications · 187 citations

    1

    h-index

  4. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 1 publications · 10 citations

1–4