GeneCytNet: an interpretable deep learning framework for rheumatoid arthritis classification and

Background: Rheumatoid arthritis (RA) is a heterogeneous autoimmune disease where cytokine-driven dysregulation of gene networks poses a significant challenge for accurate diagnosis and targeted therapy. While transcriptomic data hold immense promise, most machine learning models lack the interpretability to decipher the underlying biological mechanisms, particularly the specific roles of key cytokines. Methods: We developed GeneCytNet, a novel deep learning framework that integrates a Variational Autoencoder (VAE) for nonlinear feature compression with a Graph Attention Network (GAT) to model gene-gene interactions. The model was developed on a synthetic cohort of 240 RA and 120 healthy control samples, with an independent holdout cohort of 100 RA and 50 controls, each with 15,000 gene features, designed as a robust proof-of-concept. Performance was benchmarked against classical models, and generalizability was assessed via cross-validation and the independent holdout. Crucially, we introduced in silico cytokine perturbation experiments to simulate the effect of modulating IL-6, TNF-α, and IL-1β responsive gene modules on RA risk prediction. Results: GeneCytNet achieved superior classification performance, with a test AUC of 0.962 ± 0.005, accuracy of 0.914 ± 0.007, and an F1-score of 0.915 ± 0.006, outperforming all baseline models. Cross-validation confirmed robustness (mean AUC = 0.957 ± 0.006). The perturbation experiments provided mechanistically interpretable insights, revealing that the IL-6-responsive module had the most significant effect on RA probability (+0.12 ± 0.03), followed by TNF-α (+0.08 ± 0.02) and IL-1β (+0.06 ± 0.02). This hierarchy of cytokine effect sizes aligns with established clinical evidence. Conclusion: GeneCytNet demonstrates that advanced, interpretable deep learning can simultaneously achieve high diagnostic accuracy and generate testable biological hypotheses. By functioning as a