A Machine Learning Approach to Parkinson's Disease Blood Transcriptomics.

ABSTRACT: The increased incidence and the significant health burden associated with Parkinson's disease (PD) have stimulated substantial research efforts towards the identification of effective treatments and diagnostic procedures. Despite technological advancements, a cure is still not available and PD is often diagnosed a long time after onset when irreversible damage has already occurred. Blood transcriptomics represents a potentially disruptive technology for the early diagnosis of PD. We used transcriptome data from the PPMI study, a large cohort study with early PD subjects and age matched controls (HC), to perform the classification of PD vs. HC in around 550 samples. Using a nested feature selection procedure based on Random Forests and XGBoost we reached an AUC of 72% and found 493 candidat

SUBMITTER: Pantaleo E 

PROVIDER: S-EPMC9141063 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature