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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