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Author Haury, Anne-Claire ♦ Jacob, Laurent ♦ Vert, Jean-Philippe
Source arXiv.org
Content type Text
File Format PDF
Date of Submission 2010-01-18
Language English
Subject Domain (in DDC) Natural sciences & mathematics ♦ Mathematics ♦ Probabilities & applied mathematics ♦ Life sciences; biology
Subject Keyword Statistics - Machine Learning ♦ Quantitative Biology - Genomics ♦ Quantitative Biology - Quantitative Methods ♦ Statistics - Applications ♦ q-bio ♦ stat
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.
Educational Use Research
Learning Resource Type Article


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