Classification of microarray data using gene networks
Paper by Rapaport, Zinovyev, Dutreix, Barillot, Vert.
BMC Bioinformatics 8:35 (2007)
Talk by Michael Gutkin
Microarrays have become extremely useful for analysing genetic phenomena,
but establishing a relation between microarray analysis results (typically
a list of genes) and their biological significance is often difficult.
Currently, the standard approach is to map a posteriori the results onto
gene networks in order to elucidate the functions perturbed at the level
of pathways. However, integrating a priori knowledge of the gene networks
could help in the statistical analysis of gene expression data and in
their biological interpretation. The approach is based on the spectral
decomposition of gene expression profiles with respect to the
eigenfunctions of the graph, resulting in an attenuation of the
high-frequency components of the expression profiles with respect to the
topology of the graph.
I will present the paper by discussing the methods they used, and
explaining the theory behind them. Finally, I will show the results in the
paper.