Toward Defining Molecular Signature of Alzheimer's Disease Using a Network-Based Systems Biology Approach.

Alzheimer's Disease (AD) is a complex multifactorial disease that does not yet have a cure. Although many statistical and computational methods have been effective in its early prediction and prognosis, our understanding of the biological mechanisms of the disease is far from satisfactory. Due to the complex nature of the disease and the lack of diversity in existing data, a multimodal systems biology approach is best suited to elucidate the etiology of the disease while abstracting variant differences among populations of different ethnicities. Although Alzheimer's is a complex disease with many contributing factors, genetic risks have been shown to have a significant impact on its manifestation. Using a systems biology approach, this paper uses graph theory, network analysis, omics data integration, and visualization to develop a functionally coherent AD module. This genetic tool, combined with other existing biomarkers and predictive algorithms, can enhance the accuracy of AD early prediction and prognosis. [ABSTRACT FROM AUTHOR]