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Preprints, Working Papers, ... Year : 2010

Increasing stability and interpretability of gene expression signatures

Abstract

Motivation : Molecular signatures for diagnosis or prognosis estimated from large-scale gene expression data often lack robustness and stability, rendering their biological interpretation challenging. Increasing the signature's interpretability and stability across perturbations of a given dataset and, if possible, across datasets, is urgently needed to ease the discovery of important biological processes and, eventually, new drug targets. Results : We propose a new method to construct signatures with increased stability and easier interpretability. The method uses a gene network as side interpretation and enforces a large connectivity among the genes in the signature, leading to signatures typically made of genes clustered in a few subnetworks. It combines the recently proposed graph Lasso procedure with a stability selection procedure. We evaluate its relevance for the estimation of a prognostic signature in breast cancer, and highlight in particular the increase in interpretability and stability of the signature.
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Dates and versions

hal-00448395 , version 1 (18-01-2010)

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Anne-Claire Haury, Laurent Jacob, Jean-Philippe Vert. Increasing stability and interpretability of gene expression signatures. 2010. ⟨hal-00448395⟩
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