Relatively high predictive performance (AUC: 0.70) was observed when classifying AD and normal gene expression profiles from individuals using leave-one-out cross validation. Biological process enrichment analysis revealed the prioritized genes are modulated in AD pathogenesis including: regulation of neurogenesis and generation of neurons. This approach correctly identified key AD susceptible genes: PSEN1 and TRAF1. The pipeline was applied to prioritize Alzheimer's Disease (AD) genes, whereby a list of 32 prioritized genes was generated. Diverse heterogeneous data including: gene-expression, protein-protein interaction network, ontology-based similarity and topological measures and tissue-specific are integrated. In this paper we propose a computational pipeline for the prioritization of disease-gene candidates. The integration of protein-protein interaction networks along with disease datasets and contextual information is an important tool in unraveling the molecular basis of diseases. The application of gene prioritization can enhance our understanding of disease mechanisms and aid in the discovery of drug targets. An important challenge now is to identify meaningful disease-associated genes from a long list of candidate genes implicated by these analyses.
Genome-wide linkage and association studies have made advancements in identifying genetic variants that underpin human disease. The identification of genes and uncovering the role they play in diseases is an important and complex challenge.
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