Linked Life Data

19586856

Source:http://linkedlifedata.com/resource/pubmed/id/19586856

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
6
pubmed:dateCreated
2009-7-9
pubmed:abstractText
For the past two decades, the dominant methods to identify susceptibility genes of complex disease were linkage analysis and association study. Linkage analysis usually identifies broad intervals, which can encompass dozens to hundreds of candidate genes. Transition from quantitative trait loci to gene has been a challenge due to the absence of complete functional information for the majority of genes in this susceptibility locus and limited knowledge of the link between gene function and disease. Recently, computational biology tools that employ information extracted from public online databases have been developed. In this review, we introduced principles of DGP, GeneSeeker, Prioritizer, PROSPECTR and SUSPECTS (P and S), and Endeavor, then used the prediction of susceptibility genes for type 2 diabetes mellitus/obesity and osteoporosis as examples to elucidate the application of computational biology strategies, and finally discuss the limitations and prospects of these methods.
pubmed:language
chi
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
0253-9772
pubmed:author
pubmed:issnType
Print
pubmed:volume
31
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
581-6
pubmed:dateRevised
2009-11-19
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
[Computational biology strategy for identification of complex disease genes].
pubmed:affiliation
College of Life Sciences, Central China Normal University, Wuhan 430079, China. xiaomin163-111@163.com
pubmed:publicationType
Journal Article, English Abstract