A Multi-omics Framework Based on Machine Learning as a Predictor of Cognitive Impairment Progression in Early Parkinson's Disease.

ABSTRACT:

Introduction

Cognitive impairment (CI) is a common non-motor symptom of Parkinson's disease (PD). However, the diagnosis and prediction of CI progression in PD remain challenging. We aimed to explore a multi-omics framework based on machine learning integrating comprehensive radiomics, cerebrospinal fluid biomarkers, and genetics information to identify CI progression in early PD.

Methods

Patients were first diagnosed with PD without CI at baseline. According to whether CI progressed within 5 years, patients were divided into two groups: PD without CI and PD with CI. Radiomics signatures were extracted from patients' T1-weighted MRI. We used machine learning methods to construct radiomics, hybrid, and multi-omics models in the training set and validated the models in the tes

SUBMITTER: Luo Y 

PROVIDER: S-EPMC11906927 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature