Prediction of human functional genetic networks from heterogeneous data using RVM-based ensemble learning.
Bioinformatics
; 26(6): 807-13, 2010 Mar 15.
Article
em En
| MEDLINE
| ID: mdl-20134029
ABSTRACT
MOTIVATION Three major problems confront the construction of a human genetic network from heterogeneous genomics data using kernel-based approaches definition of a robust gold-standard negative set, large-scale learning and massive missing data values. RESULTS:
The proposed graph-based approach generates a robust GSN for the training process of genetic network construction. The RVM-based ensemble model that combines AdaBoost and reduced-feature yields improved performance on large-scale learning problems with massive missing values in comparison to Naïve Bayes. CONTACT dargenio@bmsr.usc.edu SUPPLEMENTARY INFORMATION Supplementary material is available at Bioinformatics online.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Transdução de Sinais
/
Genômica
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Bioinformatics
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2010
Tipo de documento:
Article
País de afiliação:
Estados Unidos