Autoencoder-based Fusion

Autoencoder-Based Fusion is a data integration framework that employs autoencoder architectures to jointly learn compressed, latent representations from multiple omics data types by leveraging their shared underlying biological structure to enable downstream multi-omics analysis.

91

Publications

4.3K

Citations

330

Authors

173

Institutions

Publications per year

2017–2026

89

Authors

330

  1. MS
    Madhu S. Nair

    University of Kerala, India

    2 publications · 167 citations

    2

    h-index

  2. BA
    Bejoy Abraham

    Cochin University of Science and Technology, India

    2 publications · 167 citations

    2

    h-index

  3. MJ
    María José del Jesús

    Universidad de Jaén, Spain

    1 publications · 43 citations

    1

    h-index

  4. YC
    Yung‐Yu Chuang

    National Taiwan University, Taiwan

    1 publications · 17 citations

    1

    h-index

  5. LH
    Luyao Han

    Shaanxi University of Science and Technology, China

    1 publications · 32 citations

    1

    h-index

1–5 of 330

Venues

  1. IEEE Access

    4 publications · 392 citations

    4

    h-index

  2. Information Fusion

    2 publications · 308 citations

    3

    h-index

  3. IEEE Transactions on Multimedia

    3 publications · 125 citations

    3

    h-index

  4. Applied Soft Computing

    3 publications · 88 citations

    3

    h-index

  5. 3

    h-index

1–5