Integrative Omics in Parkinson's Disease

Provides a comprehensive understanding of the current literature on high-throughput technologies relating to discoveries for Parkinson's disease. This emerging field uses large omics datasets to investigate the etiology of Parkinson's disease and other forms of parkinsonism. The book traces the evolution of omics technologies from the discovery of monogenic Parkinson's disease forms.

Chapters delve into genomics, transcriptomics, epigenomics, artificial intelligence, and gene-environment interactions. Furthermore, it examines the potential therapeutic applications of these advancements and provides insights into the future of omics research in Parkinson's disease.

Key Topics Covered

• Reviews evolution of omics technologies from the first identification of monogenic forms of Parkinson's disease
• Outlines machine learning algorithm application to Parkinson's disease datasets
• Reviews big datasets on gene-environment interactions, genomics, epigenetics, and transcriptomics
• Identifies how the microbiome influences Parkinson's disease in mouse models and patients
• Provides outlook for therapies with induced-pluripotent stem cell models