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Gene regulatory network of human adipocyte differentiation
BMC Systems Biology volume 1, Article number: P57 (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®.
The choices of pre-processing methods and dimensionality reduction techniques applied in this work have a major impact on Bayesian network extraction. The Bayesian networks extracted were validated against a proprietary Network warehouse database at GSK. Many novel genetic interactions were identified. We suggest that these pre-processing methods can be widely used genetic network extraction, even in the absence of many experiments but replicates. Thus improves the prediction of significant changes in gene expression for microarray experiments and reduce both false positives and negatives.
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Kumuthini, J., Bessant, C., Wilson, P. et al. Gene regulatory network of human adipocyte differentiation. BMC Syst Biol 1, P57 (2007). https://doi.org/10.1186/1752-0509-1-S1-P57
- Bayesian Network
- Genetic Network
- Taboo Search Algorithm
- Dimensionality Reduction Technique
- Artificial Experiment