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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Detalles Bibliográficos
Autores principales:	Michael Grau, Georg Lenz, Peter Lenz
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/e6122c78f3584f9387082cbd4ab6e29f
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spelling	oai:doaj.org-article:e6122c78f3584f9387082cbd4ab6e29f2021-12-02T16:51:01ZDissection of gene expression datasets into clinically relevant interaction signatures via high-dimensional correlation maximization10.1038/s41467-019-12713-52041-1723https://doaj.org/article/e6122c78f3584f9387082cbd4ab6e29f2019-11-01T00:00:00Zhttps://doi.org/10.1038/s41467-019-12713-5https://doaj.org/toc/2041-1723Identification 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.Michael GrauGeorg LenzPeter LenzNature PortfolioarticleScienceQENNature Communications, Vol 10, Iss 1, Pp 1-16 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Michael Grau Georg Lenz Peter Lenz Dissection of gene expression datasets into clinically relevant interaction signatures via high-dimensional correlation maximization
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.
format	article
author	Michael Grau Georg Lenz Peter Lenz
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 Portfolio
publishDate	2019
url	https://doaj.org/article/e6122c78f3584f9387082cbd4ab6e29f
work_keys_str_mv	AT michaelgrau dissectionofgeneexpressiondatasetsintoclinicallyrelevantinteractionsignaturesviahighdimensionalcorrelationmaximization AT georglenz dissectionofgeneexpressiondatasetsintoclinicallyrelevantinteractionsignaturesviahighdimensionalcorrelationmaximization AT peterlenz dissectionofgeneexpressiondatasetsintoclinicallyrelevantinteractionsignaturesviahighdimensionalcorrelationmaximization
_version_	1718383011500654592

Dissection of gene expression datasets into clinically relevant interaction signatures via high-dimensional correlation maximization

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