Causality and pathway search in microarray time series experiment.
Bioinformatics
; 23(4): 442-9, 2007 Feb 15.
Article
en En
| MEDLINE
| ID: mdl-17158516
ABSTRACT
MOTIVATION Interaction among time series can be explored in many ways. All the approach has the usual problem of low power and high dimensional model. Here we attempted to build a causality network among a set of time series. The causality has been established by Granger causality, and then constructing the pathway has been implemented by finding the Minimal Spanning Tree within each connected component of the inferred network. False discovery rate measurement has been used to identify the most significant causalities. RESULTS:
Simulation shows good convergence and accuracy of the algorithm. Robustness of the procedure has been demonstrated by applying the algorithm in a non-stationary time series setup. Application of the algorithm in a real dataset identified many causalities, with some overlap with previously known ones. Assembled network of the genes reveals features of the network that are common wisdom about naturally occurring networks.
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Banco de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Transducción de Señal
/
Análisis de Secuencia por Matrices de Oligonucleótidos
/
Proteoma
/
Perfilación de la Expresión Génica
/
Modelos Biológicos
Tipo de estudio:
Etiology_studies
/
Prognostic_studies
Idioma:
En
Revista:
Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2007
Tipo del documento:
Article