01557nas a2200169 4500008004100000022001400041245008300055210006900138260000900207300001100216490000600227520098100233100001701214700002301231700001801254856011501272 2010 eng d a1932-620300aCross-platform microarray data normalisation for regulatory network inference.0 aCrossplatform microarray data normalisation for regulatory netwo c2010 ae138220 v53 a

BACKGROUND: Inferring Gene Regulatory Networks (GRNs) from time course microarray data suffers from the dimensionality problem created by the short length of available time series compared to the large number of genes in the network. To overcome this, data integration from diverse sources is mandatory. Microarray data from different sources and platforms are publicly available, but integration is not straightforward, due to platform and experimental differences.

METHODS: We analyse here different normalisation approaches for microarray data integration, in the context of reverse engineering of GRN quantitative models. We introduce two preprocessing approaches based on existing normalisation techniques and provide a comprehensive comparison of normalised datasets.

CONCLUSIONS: Results identify a method based on a combination of Loess normalisation and iterative K-means as best for time series normalisation for this problem.

1 aSirbu, Alina1 aRuskin, Heather, J1 aCrane, Martin uhttps://kdd.isti.cnr.it/publications/cross-platform-microarray-data-normalisation-regulatory-network-inference