Systems biology or pathway-based data analysis approaches allow the identification of networks of biological entities that may collectively define mechanisms and phenotypes, especially as they relate to disease. Herein, we applied a systems biology workflow to predict clinically relevant biomarkers and drugs targets for Alzheimer’s disease. Our workflow included several <i>in silico</i> approaches that integrate ‘omics’ data mining from NCBI’s Gene Expression Omnibus, the prioritization of disease gene signatures, and the analysis of disease pathways and networks. Preliminary results led to the prioritization of tens of promising Alzheimer’s-tracking biomarkers and drug targets that were significantly up- or down- regulated in Alzheimer’s disease patients. Unbiased enrichment analyses highlighted several kinases and chemokine receptors. Biased analyses focused on Neurological disorders reduced the initial hit list into 10 higher confidence, clinically relevant, biomarkers and drugs targets (including LRRK2, BDNF and CXCR4). These final hits should serve as computational hypotheses awaiting their experimental validation.

Systems biology approaches to omics data analysis in complex diseases

253rd National Meeting (2017)

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