Document Detail

Supervised inference of gene regulatory networks from positive and unlabeled examples.
MedLine Citation:
PMID:  23192540     Owner:  NLM     Status:  In-Data-Review    
Elucidating the structure of gene regulatory networks (GRN), i.e., identifying which genes are under control of which transcription factors, is an important challenge to gain insight on a cell's working mechanisms. We present SIRENE, a method to estimate a GRN from a collection of expression data. Contrary to most existing methods for GRN inference, SIRENE requires as input a list of known regulations, in addition to expression data, and implements a supervised machine-learning approach based on learning from positive and unlabeled examples to account for the lack of negative examples.
Fantine Mordelet; Jean-Philippe Vert
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:  47-58     Citation Subset:  IM    
Department of Computer Science, Duke University, Durham, NC, USA,
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

Previous Document:  Structure learning for bayesian networks as models of biological networks.
Next Document:  Mining regulatory network connections by ranking transcription factor target genes using time series...