CO-expression Analysis of RNA-sequence Data from Parkinson's Disease Patients

Abstract

Parkinson’s disease is known as a progressive neurological disease characterized by motor symptoms. The motor symptoms are caused by neurodegeneration that causes dysfunctionalities in molecular functions crucial for movement. Network analysis contributes to identifying new biomarkers of diseases by considering the interactions between the disease-specific genes and proteins. This study focuses on a differential weighted gene co-expression network analysis of transcriptomics data, comparing data from healthy persons with Parkinson’s diseased patients. This analysis method constructs networks and identifies modules that can be compared with different evaluation and analysis methods, to identify dysregulated pathways and causative genes of Parkinson’s disease. This disease is a complex disease by multiple variations of symptoms with each individual. This study contributes to the predictive part of personalized medicine that enables improved treatments.