18537730

Source:http://linkedlifedata.com/resource/pubmed/id/18537730

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Predicate	Object
rdf:type	pubmed:Citation
lifeskim:mentions	umls-concept:C0008902, umls-concept:C0033684, umls-concept:C0040811, umls-concept:C1527178, umls-concept:C1705938, umls-concept:C1706942
pubmed:issue	5
pubmed:dateCreated	2008-6-9
pubmed:abstractText	We present two efficient network propagation algorithms that operate on a binary tree, i.e., a sparse-edged substitute of an entire similarity network. TreeProp-N is based on passing increments between nodes while TreeProp-E employs propagation to the edges of the tree. Both algorithms improve protein classification efficiency.
pubmed:language	eng
pubmed:journal	http://linkedlifedata.com/resource/pubmed/journal/9441434
pubmed:citationSubset	IM
pubmed:chemical	http://linkedlifedata.com/resource/pubmed/chemical/Proteins
pubmed:status	MEDLINE
pubmed:issn	0929-8665
pubmed:author	pubmed-author:Busa-FeketeRóbertR, pubmed-author:KocsorAndrásA, pubmed-author:PongorSándorS
pubmed:issnType	Print
pubmed:volume	15
pubmed:owner	NLM
pubmed:authorsComplete	Y
pubmed:pagination	428-34
pubmed:meshHeading	pubmed-meshheading:18537730-Algorithms, pubmed-meshheading:18537730-Computational Biology, pubmed-meshheading:18537730-Databases, Protein, pubmed-meshheading:18537730-Proteins
pubmed:year	2008
pubmed:articleTitle	Protein classification based on propagation of unrooted binary trees.
pubmed:affiliation	Hungary Academy of Sciences, Szeged, Hungary.
pubmed:publicationType	Journal Article, Research Support, Non-U.S. Gov't