Combination of a naive Bayes classifier with consensus scoring improves enrichment of high-throughput docking results.
J Med Chem
; 47(18): 4356-9, 2004 Aug 26.
Article
em En
| MEDLINE
| ID: mdl-15317449
ABSTRACT
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.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Desenho de Fármacos
/
Proteínas
/
Modelos Estatísticos
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
J Med Chem
Assunto da revista:
QUIMICA
Ano de publicação:
2004
Tipo de documento:
Article
País de afiliação:
Estados Unidos