Dimensionality Reduction Interpretation

Dimensionality Reduction Interpretation is a computational methodology within multi-omics data analysis that reduces the number of random variables under consideration while preserving biologically meaningful patterns, thereby enabling the interpretation of high-dimensional datasets through simplified, lower-dimensional representations.

55

Publications

3.2K

Citations

193

Authors

120

Institutions

Publications per year

2017–2026

25

Authors

193

  1. RD
    R. Dennis Cook

    University of Minnesota, United States

    2 publications · 290 citations

    2

    h-index

  2. CX
    Caiming Xiong

    Salesforce (United States), United States

    2 publications · 177 citations

    2

    h-index

  3. RS
    Richard Socher

    Salesforce (United States), United States

    2 publications · 177 citations

    2

    h-index

  4. NS
    Nitish Shirish Keskar

    Salesforce (United States), United States

    2 publications · 177 citations

    2

    h-index

  5. AD
    Akhilesh Deepak Gotmare

    Salesforce (United States), United States

    2 publications · 177 citations

    2

    h-index

1–5 of 193

Venues

  1. Proceedings of the National Academy of Sciences

    PNAS · 4 publications · 292 citations

    4

    h-index

  2. arXiv (Cornell University)

    Repository · 3 publications · 220 citations

    3

    h-index

  3. Bioinformatics

    3 publications · 301 citations

    3

    h-index

  4. 2

    h-index

  5. Scientific Reports

    2 publications · 64 citations

    2

    h-index

1–5