Integrative multi-omics and network-based machine learning for early diagnosis of Parkinson's disease.

ABSTRACT:

Background

Accurate diagnosis of Parkinson's Disease (PD) remains challenging due to its biological complexity. Integrating machine learning with multi-omics and network topological analyses may enhance diagnostic precision.

Objective

To classify early-stage PD patients and healthy controls (HCs) by integrating multi-omics data and network-based machine learning.

Methods

We analyzed 305 participants from the PPMI cohort (213 PD, 92 HCs) using DNA methylation, gene expression, and proteomic data. Feature selection was conducted through sPLS-DA, followed by integration via DIABLO. Regulatory gene networks were constructed using STRING-based topology analysis. An XGBoost classifiers were trained and optimized on 244 samples and validated on 61 independent samples. Furthe

SUBMITTER: Liu W 

PROVIDER: S-EPMC12773809 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature