Sciweavers

ECCB
2008
IEEE

SIRENE: supervised inference of regulatory networks

14 years 6 months ago
SIRENE: supervised inference of regulatory networks
Living cells are the product of gene expression programs that involve the regulated transcription of thousands of genes. The elucidation of transcriptional regulatory networks in thus needed to understand the cell’s working mechanism, and can for example be useful for the discovery of novel therapeutic targets. Although several methods have been proposed to infer gene regulatory networks from gene expression data, a recent comparison on a large-scale benchmark experiment revealed that most current methods only predict a limited number of known regulations at a reasonable precision level. We propose SIRENE, a new method for the inference of gene regulatory networks from a compendium of expression data. The method decomposes the problem of gene regulatory network inference into a large number of local binary classification problems, that focus on separating target genes from non-targets for each TF. SIRENE is thus conceptually simple and computationally efficient. We test it on a ben...
Fantine Mordelet, Jean-Philippe Vert
Added 29 May 2010
Updated 29 May 2010
Type Conference
Year 2008
Where ECCB
Authors Fantine Mordelet, Jean-Philippe Vert
Comments (0)