Mining regulatory network connections by ranking transcription factor target genes using time series expression data.
Methods Mol Biol
; 939: 59-67, 2013.
Article
en En
| MEDLINE
| ID: mdl-23192541
ABSTRACT
Reverse engineering the gene regulatory network is challenging because the amount of available data is very limited compared to the complexity of the underlying network. We present a technique addressing this problem through focussing on a more limited problem:
inferring direct targets of a transcription factor from short expression time series. The method is based on combining Gaussian process priors and ordinary differential equation models allowing inference on limited potentially unevenly sampled data. The method is implemented as an R/Bioconductor package, and it is demonstrated by ranking candidate targets of the p53 tumour suppressor.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Factores de Transcripción
/
Biología Computacional
/
Perfilación de la Expresión Génica
/
Redes Reguladoras de Genes
/
Minería de Datos
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Methods Mol Biol
Asunto de la revista:
BIOLOGIA MOLECULAR
Año:
2013
Tipo del documento:
Article
País de afiliación:
Finlandia