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Published 2000 | Published
Book Section - Chapter Open

From Coexpression to Coregulation: An Approach to Inferring Transcriptional Regulation among Gene Classes from Large-Scale Expression Data


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.

Additional Information

© 2000 Massachusetts Institute of Technology. This work was supported in part by the Whittier Foundation, the Office of Naval Research under contract N00014-97-1-0422, and the NASA Advanced Concepts Program. Stuart Kim (Stanford University) provided the C. elegans gene expression array data. The GRN simulation and inference code is due in part to Charles Garrett and George Marnellos. The EM clustering code is due in part to Roberto Manduchi.

Attached Files

Published - 1706-from-coexpression-to-coregulation-an-approach-to-inferring-transcriptional-regulation-among-gene-classes-from-large-scale-expression-data.pdf



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August 19, 2023
January 13, 2024