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

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.

Bibliographic Details
Main Authors: Michael Grau, Georg Lenz, Peter Lenz
Format: Article
Language:English
Published: Nature Publishing Group 2019-11-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-019-12713-5
id doaj-e6122c78f3584f9387082cbd4ab6e29f
record_format Article
spelling 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 AT michaelgrau dissectionofgeneexpressiondatasetsintoclinicallyrelevantinteractionsignaturesviahighdimensionalcorrelationmaximization
AT georglenz dissectionofgeneexpressiondatasetsintoclinicallyrelevantinteractionsignaturesviahighdimensionalcorrelationmaximization
AT peterlenz dissectionofgeneexpressiondatasetsintoclinicallyrelevantinteractionsignaturesviahighdimensionalcorrelationmaximization
_version_ 1721444526620409856

Similar Items