Prifti E. auteur aut IRD Zucker Jean-Daniel auteur aut IRD Clement K. auteur aut IRD Henegar C. auteur aut IRD text journalArticle eng Motivation: The noisy nature of transcriptomic data hinders the biological relevance of conventional network centrality measures, often used to select gene candidates in co-expression networks. Therefore, new tools and methods are required to improve the prediction of mechanistically important transcriptional targets. Results: We propose an original network centrality measure, called annotation transcriptional centrality (ATC) computed by integrating gene expression profiles from microarray experiments with biological knowledge extracted from public genomic databases. ATC computation algorithm delimits representative functional domains in the co-expression network and then relies on this information to find key nodes that modulate propagation of functional influences within the network. We demonstrate ATC ability to predict important genes in several experimental models and provide improved biological relevance over conventional topological network centrality measures. specialized 122 020 26 24 3083-3089 2010 1367-4803 https://www.documentation.ird.fr/hor/PAR00006434 10.1093/bioinformatics/btq591 1367-4803 https://www.documentation.ird.fr/hor/PAR00006434 IRD - Base Horizon / Pleins textes 2011-02-04 2017-08-23 PAR00006434 fre