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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| Autores principales: | , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2019
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/e6122c78f3584f9387082cbd4ab6e29f |
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| Sumario: | 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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