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: | , , |
---|---|
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 |
Summary: | 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. |
---|---|
ISSN: | 2041-1723 |