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

Mjolsness, Eric and Mann, Tobias and Castaño, Rebecca and Wold, Barbara (2000) From Coexpression to Coregulation: An Approach to Inferring Transcriptional Regulation among Gene Classes from Large-Scale Expression Data. In: Advances in Neural Information Processing Systems 12 (NIPS 1999). Advances in Neural Information Processing Systems. No.12. MIT Press , Cambridge, MA, pp. 928-934. ISBN 0-262-19450-3. https://resolver.caltech.edu/CaltechAUTHORS:20160229-163500753

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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.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
http://papers.nips.cc/paper/1706-from-coexpression-to-coregulation-an-approach-to-inferring-transcriptional-regulation-among-gene-classes-from-large-scale-expression-dataOrganizationPaper
ORCID:
AuthorORCID
Wold, Barbara0000-0003-3235-8130
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.
Funders:
Funding AgencyGrant Number
Whittier FoundationUNSPECIFIED
Office of Naval Research (ONR)N00014-97-1-0422
NASAUNSPECIFIED
Series Name:Advances in Neural Information Processing Systems
Issue or Number:12
Record Number:CaltechAUTHORS:20160229-163500753
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20160229-163500753
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:64880
Collection:CaltechAUTHORS
Deposited By: Kristin Buxton
Deposited On:01 Mar 2016 00:42
Last Modified:09 Mar 2020 13:19

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