Document Detail


Combination of a naive Bayes classifier with consensus scoring improves enrichment of high-throughput docking results.
MedLine Citation:
PMID:  15317449     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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.
Authors:
Anthony E Klon; Meir Glick; John W Davies
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Journal of medicinal chemistry     Volume:  47     ISSN:  0022-2623     ISO Abbreviation:  J. Med. Chem.     Publication Date:  2004 Aug 
Date Detail:
Created Date:  2004-08-19     Completed Date:  2004-09-24     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9716531     Medline TA:  J Med Chem     Country:  United States    
Other Details:
Languages:  eng     Pagination:  4356-9     Citation Subset:  IM    
Affiliation:
Novartis Institute for Biomedical Research, 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA. anthony.klon@pharma.novartis.com
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MeSH Terms
Descriptor/Qualifier:
Algorithms*
Artificial Intelligence
Databases, Protein
Drug Design*
Models, Statistical*
Protein Binding
Proteins / antagonists & inhibitors*
Chemical
Reg. No./Substance:
0/Proteins

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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