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.
Main Authors: | Michael Grau, Georg Lenz, Peter Lenz |
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Format: | Article |
Language: | English |
Published: |
Nature Publishing Group
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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