Graph neural networks for investigating complex diseases: A case study on Parkinson's Disease

Omics data analysis is a critical component in the study of complex diseases, but the high dimension and heterogeneity of the data often pose challenges that are difficult to address by classical statistical and machine learning methods. Recently, structured data analyses using graph neural networks (GNNs) have emerged as a promising complementary approach, particularly for investigating the relational information between samples. However, it is still unclear which strategies for designing and optimizing GNNs are most effective when working with real-world data from complex disorders, such as Parkinson's disease (PD).
Our study addresses this gap by examining the application of various GNN models, including Graph
Convolutional Network, ChebyNet, and Graph Attention Network, to identify and interpret
discriminative patterns between PD patients and controls using omics data. The developed pipeline
integrates Lasso penalty-based feature selection, similarity graph construction, and final modeling for sample classification. Through an end-to-end model building and evaluation process, we assess the practical utility of the pipeline on independent PD omics datasets.
Overall, our analyses highlight some of the benefits and challenges of using graph structure data for machine learning analysis of disease-related omics data and provide directions for further research.