Abstract. We have analysed gene expression data from microarray experiments of E. coli bacteria using mathematical and computing methods appropriate for disordered systems characterised by noisy and imperfect data. The patterns identified in the data suggest similarities in the gene behaviour. We propose a classification of E. coli genes based on consistently correlated genes in expression data and similarities of gene expression patterns. The classification and identification of co-expressed genes allows inferring the function of unknown genes by comparing the co-regulated genes to the genes with characterised function.

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