Linked Life Data

18312210

Source:http://linkedlifedata.com/resource/pubmed/id/18312210

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2008-3-3
pubmed:abstractText
Multiple branching trees have been used to model the acquisition of HIV drug resistance mutations, and several different algorithms have been developed to construct the tree set that best describes the data. These algorithms have mainly focused on the structure of the tree set. The focal point of this paper is estimation of functions of the tree set parameters that incorporate uncertainty in the tree set. The functions of interest are the state probabilities, the co-occurrence of mutations and the order of acquisition. Such functions are of interest because they help characterize the genetic pathways that lead to multi-drug resistance. We propose a bootstrap technique to account for the additional variability in estimates due to uncertainty in the tree set. The methods are applied to genetic sequences of patients from a database compiled by the Forum for Collaborative HIV Research in an effort to characterize genetic pathways to resistance to drugs from the nucleoside reverse transcriptase inhibitor (NRTI) class. The main results were that patients with a 211K mutation in the RT region of the viral genome were more likely to have a 215Y mutation and less likely to have a 70R mutation compared to patients without a 211K mutation.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
1544-6115
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
7
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
Article5
pubmed:dateRevised
2009-1-16
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
Accommodating uncertainty in a tree set for function estimation.
pubmed:affiliation
Harvard Medical School, USA. nvergne@genopole.cnrs.fr
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural