Data mining and knowledge discovery in predictive toxicology.
SAR QSAR Environ Res
; 15(5-6): 367-83, 2004.
Article
em En
| MEDLINE
| ID: mdl-15669696
ABSTRACT
This article describes the knowledge discovery process in predictive toxicology. This process consists of five major steps (i) feature calculation, (ii) feature selection, (iii) model induction, (iv) model validation and (v) interpretation of predictions and models. Data mining is a part of the knowledge discovery process and consists of the application of data analysis and discovery algorithms, which can be useful in all of the above steps. A brief review of suitable algorithms and their advantages and disadvantages is given for each knowledge discovery step, followed by a more detailed description of a problem-specific implementation of the lazar prediction system.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Toxicologia
/
Algoritmos
/
Conhecimento
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Animals
/
Humans
Idioma:
En
Revista:
SAR QSAR Environ Res
Assunto da revista:
SAUDE AMBIENTAL
Ano de publicação:
2004
Tipo de documento:
Article
País de afiliação:
Alemanha