Linked Life Data

19050035

Source:http://linkedlifedata.com/resource/pubmed/id/19050035

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2009-1-30
pubmed:abstractText
Genome-scale 'omics' data constitute a potentially rich source of information about biological systems and their function. There is a plethora of tools and methods available to mine omics data. However, the diversity and complexity of different omics data types is a stumbling block for multi-data integration, hence there is a dire need for additional methods to exploit potential synergy from integrated orthogonal data. Rough Sets provide an efficient means to use complex information in classification approaches. Here, we set out to explore the possibilities of Rough Sets to incorporate diverse information sources in a functional classification of unknown genes.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Feb
pubmed:issn
1367-4811
pubmed:author
pubmed:issnType
Electronic
pubmed:day
1
pubmed:volume
25
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
322-30
pubmed:dateRevised
2009-11-4
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Gene expression trends and protein features effectively complement each other in gene function prediction.
pubmed:affiliation
Department of Plant Systems Biology, VIB Technologiepark 927, 9052 Gent, Belgium. krwab@psb.ugent.be
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't