title: Cross-platform microarray data normalisation for regulatory network inference.
abstract: 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.
authors.affiliation: Centre for Scientific Computing and Complex Systems Modelling, Dublin City University, Dublin, Ireland. asirbu@computing.dcu.ie
authors.forename: Alina
authors.initials: A
authors.lastname: Sîrbu
authors.forename: Heather J
authors.initials: HJ
authors.lastname: Ruskin
authors.forename: Martin
authors.initials: M
authors.lastname: Crane
mesh_terms: D000465:Algorithms; D002453:Cell Cycle / Q000235:genetics; D019295:Computational Biology / Q000379:methods; D020869:Gene Expression Profiling / Q000379:methods*; D053263:Gene Regulatory Networks; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D015203:Reproducibility of Results; D012441:Saccharomyces cerevisiae / Q000235:genetics; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics
chemical_list: D029701:Saccharomyces cerevisiae Proteins