Gene regulatory network of human adipocyte differentiation
© Kumuthini et al; licensee BioMed Central Ltd. 2007
Published: 8 May 2007
In this article we demonstrate novel pre-processing methods to reduce data dimensionality of human adipocyte differentiation microarray data. Genetic networks of the insulin receptor family, ppar family, fox family, cebp family mef2, fabp, add1 and klf, and probes with highly significant change in gene expression level were learned separately using a Bayesian frame work. The extracted networks were validation of genetic network against many publicly available and as well as in house interaction and literature databases available at GSK.
Multidimensional, hMAD microarray data provided by GSK was used to generate additional artificial experiments using a novel technique and the differentially expressed probes were filtered. Through Gaussian clustering 45 clusters plus the outliers extracted were used to learn the genetic network using taboo search algorithm using BayesiaLab®.
This article is published under license to BioMed Central Ltd.