Linked Life Data

20052904

Source:http://linkedlifedata.com/resource/pubmed/id/20052904

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2010-1-7
pubmed:abstractText
In this paper, Cox's proportional hazards model with Lp penalty method is developed for simultaneous gene selection and survival prediction. Lp penalty shrinks coefficients and produces some coefficients that are exactly zero, and therefore can be used to identify survival related downstream genes. We also define a novel similarity measure to hunt the regulatory genes that their gene expression changes may be low but they are highly correlated with the selected genes. Experimental results with gene expression data demonstrate that the proposed procedures can be used for identifying important gene clusters that are related to time to death due to cancer and for building parsimonious model for predicting the survival of future patients.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1748-5673
pubmed:author
pubmed:issnType
Print
pubmed:volume
3
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
398-408
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Gene identification and survival prediction with Lp Cox regression and novel similarity measure.
pubmed:affiliation
University of Maryland Greenebaum Cancer Center, Baltimore, MD 21201, USA. zliu@umm.edu
pubmed:publicationType
Journal Article