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Abstract:
We provide preliminary evidence that existing algorithms for
inferring small-scale gene regulation networks from gene expression
data can be adapted to large-scale gene expression data coming from
hybridization microarrays. The essential steps are (1) clustering
many genes by their expression time-course data into a minimal set
of clusters of co-expressed genes, (2) theoretically modeling the
various conditions under which the time-courses are measured using
a continious-time analog recurrent neural network for the cluster
mean time-courses, (3) fitting such a regulatory model to the
cluster mean time courses by simulated annealing with weight decay,
and (4) analysing several such fits for commonalities in the
circuit parameter sets including the connection matrices. This
procedure can be used to assess the adequacy of existing and future
gene expression time-course data sets for determining
transcriptional regulatory relationships such as
coregulation.
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