A network-based feature selection approach to identify metabolic signatures in disease.
J Theor Biol
; 310: 216-22, 2012 Oct 07.
Article
en En
| MEDLINE
| ID: mdl-22771628
The identification and interpretation of metabolic biomarkers is a challenging task. In this context, network-based approaches have become increasingly a key technology in systems biology allowing to capture complex interactions in biological systems. In this work, we introduce a novel network-based method to identify highly predictive biomarker candidates for disease. First, we infer two different types of networks: (i) correlation networks, and (ii) a new type of network called ratio networks. Based on these networks, we introduce scores to prioritize features using topological descriptors of the vertices. To evaluate our method we use an example dataset where quantitative targeted MS/MS analysis was applied to a total of 52 blood samples from 22 persons with obesity (BMI >30) and 30 healthy controls. Using our network-based feature selection approach we identified highly discriminating metabolites for obesity (F-score >0.85, accuracy >85%), some of which could be verified by the literature.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Redes y Vías Metabólicas
/
Metabolómica
/
Obesidad
Tipo de estudio:
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Adult
/
Humans
/
Middle aged
Idioma:
En
Revista:
J Theor Biol
Año:
2012
Tipo del documento:
Article
País de afiliación:
Austria
Pais de publicación:
Reino Unido