2010/01/18 by Anne-Claire Haury, Laurent Jacob, Haury, Anne-Claire +3
Biochemistry, Genetics and Molecular Biology · #Applications (stat.AP) #Bioinformatics and Genomic Networks #FOS: Biological sciences #FOS: Computer and information sciences #Gene expression and cancer classification #Genomics (q-bio.GN) #Machine Learning (stat.ML) #Molecular Biology Techniques and Applications #Quantitative Methods (q-bio.QM)
paper · pdf · doi:10.48550/arxiv.1001.3109
openalex publication_date 2010/01/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.