Joint Embedding

Joint Embedding is a computational framework that integrates multiple single-cell omics datasets by mapping them into a shared latent space, enabling comparative analysis while preserving biological variation specific to each dataset.

205

Publications

18.2K

Citations

700

Authors

256

Institutions

Publications per year

2017–2026

173

Authors

700

  1. AZ
    Andrew Zisserman

    University of Oxford, United Kingdom

    7 publications · 494 citations

    7

    h-index

  2. CE
    Carey E. Priebe

    Johns Hopkins University, United States

    5 publications · 161 citations

    5

    h-index

  3. BS
    Bernt Schiele

    Max Planck Institute for Informatics, Germany

    5 publications · 1.1K citations

    5

    h-index

  4. TX
    Tao Xiang

    Queen Mary University of London, United Kingdom

    5 publications · 1K citations

    5

    h-index

  5. ZA
    Zeynep Akata

    Max Planck Institute for Informatics, Germany

    5 publications · 1.1K citations

    5

    h-index

1–5 of 700

Venues

  1. arXiv (Cornell University)

    Repository · 34 publications · 2.7K citations

    21

    h-index

  2. IEEE Transactions on Multimedia

    7 publications · 428 citations

    7

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 4 publications · 693 citations

    4

    h-index

  4. Bioinformatics

    4 publications · 399 citations

    4

    h-index

  5. IEEE Transactions on Cybernetics

    4 publications · 773 citations

    4

    h-index

1–5