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.
journal_name
BMC Med Genomicsjournal_title
BMC medical genomicsauthors
Squillario M,Barla Adoi
10.1186/1755-8794-4-55subject
Has Abstractpub_date
2011-07-05 00:00:00pages
55issn
1755-8794pii
1755-8794-4-55journal_volume
4pub_type
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journal_title:BMC medical genomics
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