Ensemble transcript interaction networks: A case study on Alzheimer's disease

Armañanzas Arnedillo, Ruben and Larrañaga Múgica, Pedro and Bielza Lozoya, Maria Concepcion (2012). Ensemble transcript interaction networks: A case study on Alzheimer's disease. "Computer Methods And Programs in Biomedicine", v. 108 (n. 1); pp. 442-450. ISSN 0169-2607. https://doi.org/10.1016/j.cmpb.2011.11.011.

Description

Title: Ensemble transcript interaction networks: A case study on Alzheimer's disease
Author/s:
  • Armañanzas Arnedillo, Ruben
  • Larrañaga Múgica, Pedro
  • Bielza Lozoya, Maria Concepcion
Item Type: Article
Título de Revista/Publicación: Computer Methods And Programs in Biomedicine
Date: 2012
ISSN: 0169-2607
Volume: 108
Subjects:
Faculty: Facultad de Informática (UPM)
Department: Otro
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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

More information

Item ID: 13973
DC Identifier: http://oa.upm.es/13973/
OAI Identifier: oai:oa.upm.es:13973
DOI: 10.1016/j.cmpb.2011.11.011
Deposited by: Memoria Investigacion
Deposited on: 21 Dec 2012 10:45
Last Modified: 21 Apr 2016 13:25
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