Manifold Learning Visualization

Manifold Learning Visualization is a computational technique within single-cell data analysis that reduces high-dimensional gene expression profiles into lower-dimensional embeddings by preserving intrinsic geometric structures, thereby enabling the visualization of cell-level relationships as distinct patterns in two- or three-dimensional space.

8

Publications

636

Citations

29

Authors

14

Institutions

Publications per year

2017–2026

1

Authors

29

  1. DH
    Danfeng Hong

    Chinese Academy of Sciences, China

    1 publications · 53 citations

    1

    h-index

  2. BA
    Banchar Arnonkijpanich

    Khon Kaen University, Thailand

    1 publications · 10 citations

    1

    h-index

  3. AP
    Adam Pacholski

    University of Michigan, United States

    1 publications · 32 citations

    1

    h-index

  4. SK
    Samuel Kaski

    Aalto University, Finland

    1 publications · 270 citations

    1

    h-index

  5. NP
    Neal Patwari

    University of Utah, United States

    1 publications · 32 citations

    1

    h-index

1–5 of 29

Venues

  1. IEEE Geoscience and Remote Sensing Letters

    GRSL · 1 publications · 53 citations

    1

    h-index

  2. Plasma Physics and Controlled Fusion

    1 publications · 17 citations

    1

    h-index

  3. Journal of Machine Learning Research

    JMLR · 1 publications · 181 citations

    1

    h-index

  4. IEEE Transactions on Neural Networks

    1 publications · 27 citations

    1

    h-index

  5. IEEE Intelligent Systems

    1 publications · 46 citations

    1

    h-index

1–5