Unsupervised Deconvolution

Unsupervised Deconvolution is a computational approach within cell-type deconvolution that infers cell-type composition and signatures directly from bulk tissue expression data without relying on pre-defined reference profiles or labeled training data.

27

Publications

1.5K

Citations

327

Authors

117

Institutions

Publications per year

2017–2026

21

Authors

327

  1. RC
    Robert Clarke

    Georgetown University, United States

    4 publications · 232 citations

    4

    h-index

  2. LC
    Lulu Chen

    Huazhong University of Science and Technology, China

    3 publications · 109 citations

    3

    h-index

  3. YW
    Yue Wang

    Virginia Tech, United States

    3 publications · 172 citations

    3

    h-index

  4. NW
    Niya Wang

    Hunan University, China

    2 publications · 95 citations

    2

    h-index

  5. DM
    David M. Herrington

    Wake Forest University, United States

    2 publications · 49 citations

    2

    h-index

1–5 of 327

Venues

  1. Bioinformatics

    3 publications · 109 citations

    3

    h-index

  2. Proceedings of the National Academy of Sciences

    PNAS · 2 publications · 379 citations

    2

    h-index

  3. BMC Bioinformatics

    2 publications · 55 citations

    2

    h-index

  4. Pharmacological Research

    1 publications · 19 citations

    1

    h-index

  5. Origins of Life and Evolution of Biospheres

    1 publications · 29 citations

    1

    h-index

1–5