Linked Life Data

18048187

Source:http://linkedlifedata.com/resource/pubmed/id/18048187

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2007-11-30
pubmed:abstractText
Understanding how genes are functionally related requires efficient algorithms to model networks from expression data. We report a heuristic search algorithm called Two-Level Simulated Annealing (TLSA) that is more likely to find the global optimal network structure compared to conventional simulated annealing and other searching schemes. We have applied this method to search for a global optimised network structure from a synthetic data set and an expression data set of S. cerevisiae mutants. We have achieved better precision and recall compared to other searching algorithms and are able to map relationships more accurately among functionally-linked genes.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1744-5485
pubmed:author
pubmed:issnType
Print
pubmed:volume
3
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
170-86
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Modelling gene functional linkages using yeast microarray data.
pubmed:affiliation
Department of Computer Science and Engineering, Arizona State University, Tempe, Arizona 85287, USA. tie.wang@asu.edu
pubmed:publicationType
Journal Article