Description:

Parkinson’s disease (PD) is the second prevalent neurodegenerative disorder after Alzheimer’s disease (AD), is affected 1–5% of the general population and 1-2% of the world population above 65 years, increasing to 4% over the age of 85. Over 20 genes have been associated with familial PD. 5–10% of cases are only inherited in familial PD with mutations identified in several genes such as leucine-rich repeat kinase 2 (LRRK2), glucocerebrosidase (GBA), alpha-synuclein (SNCA), Parkin (PRKN), PTEN-induced kinase 1 (PINK1), DJ-1, and VPS35. In this study, the PD genome is analyzed using whole-exome sequence (WES) and we will investigate the relationship between mutations in PD patients with clinical, motor, non-motor features as well as Radiomics features extracted from DAT SPECT images. We will also classify PD subgroups using a combination of semi-supervised methods and clustering. In the first stage, genetic data of patients will be analyzed by WES method. We employed 298 features, including 55 clinical features (CFs, motor and non-motor symptoms), 28 conventional imaging features (CIFs) and 215 RFs extracted from each ROI of DAT-SPECT image using our standardized SERA software. In this study, three groups of algorithms were employed, including 3 semi-supervised algorithms (SSA) linked with 10 Feature Selection algorithms (FSAs) and 11 Feature Extraction algorithms (FEAs). The hyperparameters of these models were tuned using a 5-fold cross-validation technique and grid search.