ArticleBMC medical genomics2011
A computational procedure for functional characterization of potential marker genes from molecular data: Alzheimer's as a case study.
Article in BMC medical genomics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Cardiovascular peptide markers are associated with age-related macular degeneration in the Bialystok PLUS general population cohort.Scientific reports · 2026Article
- Single-nucleus and spatial transcriptomic profiling of human temporal cortex and white matter reveals key associations with AD pathology.Nature communications · 2025Article
- Transplantation Induces Profound Changes in the Transcriptional Asset of Hematopoietic Stem Cells: Identification of Specific Signatures Using Machine Learning Techniques.Journal of clinical medicine · 2020Article
- A Novel Relationship for Schizophrenia, Bipolar, and Major Depressive Disorder. Part 8: a Hint from Chromosome 8 High Density Association Screen.Molecular neurobiology · 2017Article
- Enhancing Interpretability of Gene Signatures with Prior Biological Knowledge.Microarrays (Basel, Switzerland) · 2016Article
- Knowledge Driven Variable Selection (KDVS) - a new approach to enrichment analysis of gene signatures obtained from high-throughput data.Source code for biology and medicine · 2013Article
- Tissue-based Alzheimer gene expression markers-comparison of multiple machine learning approaches and investigation of redundancy in small biomarker sets.BMC bioinformatics · 2012Article
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Authors and funding
2 authors.
Funding
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Abstract
backgroundA 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.
resultsFor 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.
conclusionsThe identification of three gene signatures showing a relevant overlap of pathways and ontologies, increases the likelihood of finding potential marker genes for AD.
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