Linked Life Data

18689812

Source:http://linkedlifedata.com/resource/pubmed/id/18689812

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
16
pubmed:dateCreated
2008-8-11
pubmed:abstractText
Computational gene prioritization methods are useful to help identify susceptibility genes potentially being involved in genetic disease. Recently, text mining techniques have been applied to extract prior knowledge from text-based genomic information sources and this knowledge can be used to improve the prioritization process. However, the effect of various vocabularies, representations and ranking algorithms on text mining for gene prioritization is still an issue that requires systematic and comparative studies. Therefore, a benchmark study about the vocabularies, representations and ranking algorithms in gene prioritization by text mining is discussed in this article.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Aug
pubmed:issn
1367-4811
pubmed:author
pubmed:issnType
Electronic
pubmed:day
15
pubmed:volume
24
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
i119-25
pubmed:dateRevised
2009-11-4
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
Comparison of vocabularies, representations and ranking algorithms for gene prioritization by text mining.
pubmed:affiliation
Department of Electrical Engineering, Bioinformatics Group, SCD, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, B-3001 Heverlee, Belgium. shi.yu@esat.kuleuven.be
pubmed:publicationType
Journal Article, Comparative Study, Research Support, Non-U.S. Gov't