A Hybrid Machine Learning and Network Analysis Approach Reveals Two Parkinson's Disease Subtypes from 115 RNA-Seq Post-Mortem Brain Samples.

ABSTRACT: Precision medicine emphasizes fine-grained diagnostics, taking individual variability into account to enhance treatment effectiveness. Parkinson’s disease (PD) heterogeneity among individuals proves the existence of disease subtypes, so subgrouping patients is vital for better understanding disease mechanisms and designing precise treatment. The purpose of this study was to identify PD subtypes using RNA-Seq data in a combined pipeline including unsupervised machine learning, bioinformatics, and network analysis. Two hundred and ten post mortem brain RNA-Seq samples from PD (n = 115) and normal controls (NCs, n = 95) were obtained with systematic data retrieval following PRISMA statements and a fully data-driven clustering pipeline was performed to identify PD subtypes. Bioinformatics and

SUBMITTER: Termine A 

PROVIDER: S-EPMC8910747 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature