Dissection of gene expression datasets into clinically relevant interaction signatures via high-dimensional correlation maximization
Identification of clinically relevant gene expression signatures for cancer stratification remains challenging. Here, the authors introduce a flexible nonlinear signal superposition model that enables dissection of large gene expression data sets into signatures and extraction of gene interactions.
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2019-11-01
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Series: | Nature Communications |
Online Access: | https://doi.org/10.1038/s41467-019-12713-5 |
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doaj-e6122c78f3584f9387082cbd4ab6e29f2021-05-11T12:36:39ZengNature Publishing GroupNature Communications2041-17232019-11-0110111610.1038/s41467-019-12713-5Dissection of gene expression datasets into clinically relevant interaction signatures via high-dimensional correlation maximizationMichael Grau0Georg Lenz1Peter Lenz2Department of Medicine A, Albert-Schweitzer Campus 1, University Hospital MünsterDepartment of Medicine A, Albert-Schweitzer Campus 1, University Hospital MünsterDepartment of Physics, Renthof 5, University of MarburgIdentification of clinically relevant gene expression signatures for cancer stratification remains challenging. Here, the authors introduce a flexible nonlinear signal superposition model that enables dissection of large gene expression data sets into signatures and extraction of gene interactions.https://doi.org/10.1038/s41467-019-12713-5 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Michael Grau Georg Lenz Peter Lenz |
spellingShingle |
Michael Grau Georg Lenz Peter Lenz Dissection of gene expression datasets into clinically relevant interaction signatures via high-dimensional correlation maximization Nature Communications |
author_facet |
Michael Grau Georg Lenz Peter Lenz |
author_sort |
Michael Grau |
title |
Dissection of gene expression datasets into clinically relevant interaction signatures via high-dimensional correlation maximization |
title_short |
Dissection of gene expression datasets into clinically relevant interaction signatures via high-dimensional correlation maximization |
title_full |
Dissection of gene expression datasets into clinically relevant interaction signatures via high-dimensional correlation maximization |
title_fullStr |
Dissection of gene expression datasets into clinically relevant interaction signatures via high-dimensional correlation maximization |
title_full_unstemmed |
Dissection of gene expression datasets into clinically relevant interaction signatures via high-dimensional correlation maximization |
title_sort |
dissection of gene expression datasets into clinically relevant interaction signatures via high-dimensional correlation maximization |
publisher |
Nature Publishing Group |
series |
Nature Communications |
issn |
2041-1723 |
publishDate |
2019-11-01 |
description |
Identification of clinically relevant gene expression signatures for cancer stratification remains challenging. Here, the authors introduce a flexible nonlinear signal superposition model that enables dissection of large gene expression data sets into signatures and extraction of gene interactions. |
url |
https://doi.org/10.1038/s41467-019-12713-5 |
work_keys_str_mv |
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1721444526620409856 |