Learning a Probabilistic Boolean Network model from biological pathways and time-series expression data.
Annu Int Conf IEEE Eng Med Biol Soc
; 2016: 1471-1475, 2016 Aug.
Article
em En
| MEDLINE
| ID: mdl-28268604
ABSTRACT
The problem of inferring a stochastic model for gene regulatory networks is addressed here. The prior biological data includes biological pathways and time-series expression data. We propose a novel algorithm to use both of these data to construct a Probabilistic Boolean Network (PBN) which models the observed dynamics of genes with a high degree of precision. Our algorithm constructs a pathway tree and uses the time-series expression data to select an optimal level of tree, whose nodes are used to infer the PBN.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Modelos Genéticos
Tipo de estudo:
Risk_factors_studies
Idioma:
En
Revista:
Annu Int Conf IEEE Eng Med Biol Soc
Ano de publicação:
2016
Tipo de documento:
Article