title: A computational procedure for functional characterization of potential marker genes from molecular data: Alzheimer's as a case study.
abstract: BACKGROUND
  A molecular characterization of Alzheimer's Disease (AD) is the key to the identification of altered gene sets that lead to AD progression. We rely on the assumption that candidate marker genes for a given disease belong to specific pathogenic pathways, and we aim at unveiling those pathways stable across tissues, treatments and measurement systems. In this context, we analyzed three heterogeneous datasets, two microarray gene expression sets and one protein abundance set, applying a recently proposed feature selection method based on regularization.
  
  
  RESULTS
  For each dataset we identified a signature that was successively evaluated both from the computational and functional characterization viewpoints, estimating the classification error and retrieving the most relevant biological knowledge from different repositories. Each signature includes genes already known to be related to AD and genes that are likely to be involved in the pathogenesis or in the disease progression. The integrated analysis revealed a meaningful overlap at the functional level.
  
  
  CONCLUSIONS
  The identification of three gene signatures showing a relevant overlap of pathways and ontologies, increases the likelihood of finding potential marker genes for AD.
authors: Department of Computer and Information Science (DISI), Università degli Studi di Genova, Via Dodecaneso 35, Genova, I-16146, Italy. margherita.squillario@unige.it
authors: Margherita
authors: M
authors: Squillario
authors: Annalisa
authors: A
authors: Barla
mesh_terms: D000544:Alzheimer Disease / Q000235:genetics* / Q000473:pathology; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D018450:Disease Progression; D005819:Genetic Markers / Q000235:genetics*; D006801:Humans
chemical_list: D005819:Genetic Markers