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
Parents
27
Brian Gill, David J. Pisapia, Hani Malone et al. · Proceedings of the National Academy of Sciences · 2014 · 305 citations · Full text
Robust blind spectral unmixing for fluorescence microscopy using unsupervised learning
Tristan D. McRae, David Oleksyn, Jim Miller et al. · PLoS ONE · 2019 · 77 citations · Full text
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Virginia Tech
Blacksburg, United States
4 h-index
17 publications
824 citations
University of Copenhagen
Copenhagen, Denmark
3 h-index
11 publications
260 citations

Wake Forest University
Winston-Salem, United States
2 h-index
2 publications
49 citations

Centre National de la Recherche Scientifique
Paris, France
2 h-index
3 publications
58 citations

University of Minnesota
Minneapolis, United States
2 h-index
2 publications
137 citations
University of California, Los Angeles
Los Angeles, United States
2 h-index
20 publications
1.5K citations

Cedars-Sinai Medical Center
Los Angeles, United States
2 h-index
17 publications
1.9K citations

Hormel (United States)
Austin, United States
2 h-index
2 publications
137 citations
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3 publications · 109 citations
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PNAS · 2 publications · 379 citations
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2 publications · 55 citations
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1 publications · 19 citations
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1 publications · 29 citations
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