Linked Life Data

18354736

Source:http://linkedlifedata.com/resource/pubmed/id/18354736

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PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2008-3-20
pubmed:abstractText
The paper presents MRNET, an original method for inferring genetic networks from microarray data. The method is based on maximum relevance/minimum redundancy (MRMR), an effective information-theoretic technique for feature selection in supervised learning. The MRMR principle consists in selecting among the least redundant variables the ones that have the highest mutual information with the target. MRNET extends this feature selection principle to networks in order to infer gene-dependence relationships from microarray data. The paper assesses MRNET by benchmarking it against RELNET, CLR, and ARACNE, three state-of-the-art information-theoretic methods for large (up to several thousands of genes) network inference. Experimental results on thirty synthetically generated microarray datasets show that MRNET is competitive with these methods.
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:status
PubMed-not-MEDLINE
pubmed:issn
1687-4145
pubmed:author
pubmed:issnType
Print
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
79879
pubmed:dateRevised
2011-9-15
pubmed:year
2007
pubmed:articleTitle
Information-theoretic inference of large transcriptional regulatory networks.
pubmed:affiliation
ULB Machine Learning Group, Computer Science Department, Université Libre de Bruxelles, Brussels, Belgium.
pubmed:publicationType
Journal Article