Linked Life Data

15317449

Source:http://linkedlifedata.com/resource/pubmed/id/15317449

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
18
pubmed:dateCreated
2004-8-19
pubmed:abstractText
We have previously shown that a machine learning technique can improve the enrichment of high-throughput docking (HTD) results. In the previous cases studied, however, the application of a naive Bayes classifier failed to improve enrichment for instances where HTD alone was unable to generate an acceptable enrichment. We present here a protocol to rescue poor docking results a priori using a combination of rank-by-median consensus scoring and naive Bayesian categorization.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Aug
pubmed:issn
0022-2623
pubmed:author
pubmed:issnType
Print
pubmed:day
26
pubmed:volume
47
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
4356-9
pubmed:meshHeading
pubmed:year
2004
pubmed:articleTitle
Combination of a naive Bayes classifier with consensus scoring improves enrichment of high-throughput docking results.
pubmed:affiliation
Novartis Institute for Biomedical Research, 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA. anthony.klon@pharma.novartis.com
pubmed:publicationType
Journal Article