Statistical and Machine Learning Approaches for Network Analysis

9.1 Introduction

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by amyloid beta plaques and neurofibrillary tangles. It is not only the principal cause of dementia in the United States but also one of the fastest growing diseases in the developed countries [1]. Currently, there are over 4 million Americans diagnosed with AD and the number is estimated to double during the next 25 years [1]. In an AD brain, the cortex shrivels damaging the areas of cognition, planning, and memory [2]. Further, the hippocampus of the cortex shrinks hindering formation of new memory. The molecular mechanisms underlying the pathologies of AD are being uncovered [3,4]. Thus far, familial AD (<60 years old) which accounts for less than 5% of AD cases is due to mutations in amyloid precursor protein (APP), presenilin-1 and presenilin-2, whereas sporadic AD (>65 years old) which accounts for more than 95% cases of AD, is genetically linked to apolipoprotein E isoform 4. Despite these recent progresses in the characterization of the pathologies of AD, existing treatments for AD are far from satisfactory [5]. A more comprehensive understanding of the molecular mechanisms underlying AD is needed for better identification of molecular targets as well as development of more effective therapeutics.

In recent years, network-based methods have been widely applied to identify biomarkers or targets for various diseases [6], typically by integrating gene expression data and available ...