Interactional and functional centrality in transcriptional co-expression networks.
Bioinformatics
; 26(24): 3083-9, 2010 Dec 15.
Article
em En
| MEDLINE
| ID: mdl-20959383
ABSTRACT
MOTIVATION The noisy nature of transcriptomic data hinders the biological relevance of conventional network centrality measures, often used to select gene candidates in co-expression networks. Therefore, new tools and methods are required to improve the prediction of mechanistically important transcriptional targets. RESULTS:
We propose an original network centrality measure, called annotation transcriptional centrality (ATC) computed by integrating gene expression profiles from microarray experiments with biological knowledge extracted from public genomic databases. ATC computation algorithm delimits representative functional domains in the co-expression network and then relies on this information to find key nodes that modulate propagation of functional influences within the network. We demonstrate ATC ability to predict important genes in several experimental models and provide improved biological relevance over conventional topological network centrality measures.AVAILABILITY:
ATC computational routine is implemented in a publicly available tool named FunNet (www.funnet.info).
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Perfilação da Expressão Gênica
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Redes Reguladoras de Genes
Tipo de estudo:
Prognostic_studies
Idioma:
En
Ano de publicação:
2010
Tipo de documento:
Article