Linked Life Data

17597909

Source:http://linkedlifedata.com/resource/pubmed/id/17597909

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
8
pubmed:dateCreated
2007-6-28
pubmed:abstractText
We describe a novel and potentially important tool for candidate subunit vaccine selection through in silico reverse-vaccinology. A set of Bayesian networks able to make individual predictions for specific subcellular locations is implemented in three pipelines with different architectures: a parallel implementation with a confidence level-based decision engine and two serial implementations with a hierarchical decision structure, one initially rooted by prediction between membrane types and another rooted by soluble versus membrane prediction. The parallel pipeline outperformed the serial pipeline, but took twice as long to execute. The soluble-rooted serial pipeline outperformed the membrane-rooted predictor. Assessment using genomic test sets was more equivocal, as many more predictions are made by the parallel pipeline, yet the serial pipeline identifies 22 more of the 74 proteins of known location.
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:status
PubMed-not-MEDLINE
pubmed:issn
0973-2063
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
1
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
285-9
pubmed:dateRevised
2009-11-18
pubmed:year
2006
pubmed:articleTitle
Combining algorithms to predict bacterial protein sub-cellular location: Parallel versus concurrent implementations.
pubmed:affiliation
The Jenner Institute, University of Oxford, Compton, Newbury, Berkshire, RG20 7NN, UK.
pubmed:publicationType
Journal Article