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Mining regulatory network connections by ranking transcription factor target genes using time series expression data.
MedLine Citation:
PMID:  23192541     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
Reverse engineering the gene regulatory network is challenging because the amount of available data is very limited compared to the complexity of the underlying network. We present a technique addressing this problem through focussing on a more limited problem: inferring direct targets of a transcription factor from short expression time series. The method is based on combining Gaussian process priors and ordinary differential equation models allowing inference on limited potentially unevenly sampled data. The method is implemented as an R/Bioconductor package, and it is demonstrated by ranking candidate targets of the p53 tumour suppressor.
Authors:
Antti Honkela; Magnus Rattray; Neil D Lawrence
Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Methods in molecular biology (Clifton, N.J.)     Volume:  939     ISSN:  1940-6029     ISO Abbreviation:  Methods Mol. Biol.     Publication Date:  2013  
Date Detail:
Created Date:  2012-11-29     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9214969     Medline TA:  Methods Mol Biol     Country:  United States    
Other Details:
Languages:  eng     Pagination:  59-67     Citation Subset:  IM    
Affiliation:
Department of Computer Science, Helsinki Institute for Information Technology HIIT, University of Helsinki, Helsinki, Finland, antti.honkela@hiit.fi.
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