Trans-vatabnet: transcriptomic signatures for Parkinson's disease detection using deep learning.

Background

Early detection of Parkinson's disease (PD) is essential for timely clinical intervention. However, transcriptomic-based diagnosis faces challenges associated with high-dimensional gene expression data, redundant features, class imbalance, and limited interpretability.

Objective

This study proposes TransVaTabNet, an integrated framework for PD detection and biomarker identification using transcriptomic analysis, deep learning, and explainable artificial intelligence.

Methods

RNA-seq data were preprocessed using low-expression gene filtering, Trimmed Mean of M-values (TMM) normalization, and voom transformation. Differentially expressed genes (DEGs) were identified using limma-voom, followed by stage-specific gene analysis and functional enrichment to investigate disease-associated pathways. A Variational Autoencoder (VAE) generated compact latent representations, while the Synthetic Minority Over-sampling Technique (SMOTE) addressed class imbalance. TabNet classified the derived features using sequential attention, and SHapley Additive exPlanations (SHAP) supported model interpretation and potential biomarker identification.

Results

Evaluation on the GSE216281 RNA-seq dataset achieved an Area Under the Receiver Operating Characteristic Curve (AUC) of 90.95%, outperforming the classical machine learning models evaluated in this study.

Conclusion

TransVaTabNet combines dimensionality reduction, attention-based classification, and explainability to support PD detection and potential biomarker discovery. The framework offers an interpretable approach to identifying disease-associated transcriptomic patterns and may support future precision diagnostic research.