Memorias de investigación
Artículos en revistas:
Ensemble transcript interaction networks: A case study on Alzheimer's disease
Año:2012
Áreas de investigación
  • Inteligencia artificial,
  • Demencias seniles (alzheimer, parkinson...)
Datos
Descripción
Systems biology techniques are a topic of recent interest within the neurological field. Computational intelligence (CI) addresses this holistic perspective by means of consensus or ensemble techniques ultimately capable of uncovering new and relevant findings. In this paper, we propose the application of a CI approach based on ensemble Bayesian network classifiers and multivariate feature subset selection to induce probabilistic dependences that could match or unveil biological relationships. The research focuses on the analysis of high-throughput Alzheimer's disease (AD) transcript profiling. The analysis is conducted from two perspectives. First, we compare the expression profiles of hippocampus subregion entorhinal cortex (EC) samples of AD patients and controls. Second, we use the ensemble approach to study four types of samples: EC and dentate gyrus (DG) samples from both patients and controls. Results disclose transcript interaction networks with remarkable structures and genes not directly related to AD by previous studies. The ensemble is able to identify a variety of transcripts that play key roles in other neurological pathologies. Classical statistical assessment by means of non-parametric tests confirms the relevance of the majority of the transcripts. The ensemble approach pinpoints key metabolic mechanisms that could lead to new findings in the pathogenesis and development of AD.
Internacional
Si
JCR del ISI
Si
Título de la revista
Computer Methods And Programs in Biomedicine
ISSN
0169-2607
Factor de impacto JCR
1,238
Información de impacto
Datos JCR del año 2010
Volumen
108
DOI
10.1016/j.cmpb.2011.11.011
Número de revista
1
Desde la página
442
Hasta la página
450
Mes
SIN MES
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Participantes
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: COMPUTATIONAL INTELLIGENCE GROUP
  • Departamento: Inteligencia Artificial