In this approach, one scans for patterns in gene expression data from time-series experiments and from experiments conducted across several different conditions. If a gene is upregulated following an increased production of a transcription factor, or down-regulated following a knockout of a transcription factor, a regulatory interaction between the two is inferred to be present. In the case of expression analysis of genes from datasets of different experimental conditions, one infers sets of genes with a similar expression profile across many conditions to be co-regulated by the same set of transcription factors. Such inferences become more accurate as the number of measurements over a certain period of time (or the time- scale resolution of the data) increases.

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