Linked Life Data

20375447

Source:http://linkedlifedata.com/resource/pubmed/id/20375447

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2010-4-8
pubmed:abstractText
microRNAs are short RNA fragments that have the capacity of regulating hundreds of target gene expression. Currently, due to lack of high-throughput experimental methods for miRNA target identification, a collection of computational target prediction approaches have been developed. However, these approaches deal with different features or factors are weighted differently resulting in diverse range of predictions. The prediction accuracy remains uncertain. In this paper, three commonly used target prediction algorithms are evaluated and further integrated using algorithm combination, ranking aggregation and Bayesian Network classification. Our results revealed that each individual prediction algorithm displays its advantages as was shown on different test data sets. Among different integration strategies, the application of Bayesian Network classifier on the features calculated from multiple prediction methods significantly improved target prediction accuracy.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
1613-4516
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
7
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Comparison and integration of target prediction algorithms for microRNA studies.
pubmed:affiliation
Section Imaging and BioInformatics, Leiden Institute of Advanced Computer Science,Leiden University, The Netherlands. yanju@liacs.nl
pubmed:publicationType
Journal Article, Comparative Study