SA-FGPN: Stability-aware feature-gated progressive learning for pediatric brain tumor classification from high dimensional gene expression data.

Abstract

Objectives: Accurate pediatric brain tumor classification from gene expression data remains challenging due to the extremely high-dimensional nature of transcriptomic datasets, where tens of thousands of features are available from only a limited number of samples. This imbalance increases the risk of overfitting, unstable feature selection, and unreliable performance estimation. Moreover, molecular heterogeneity among pediatric brain tumor subtypes makes robust prediction and biological interpretation difficult. This study aims to develop a stable and interpretable deep learning framework for pediatric brain tumor classification from transcriptomic profiles.

Methods: We propose a Stability-Aware Feature-Gated Progressive Network (SA-FGPN) that integrates leakage-controlled preprocessing, low-rank transcriptomic compression, progressive nonlinear dimensionality reduction, residual projection bottleneck learning, feature-interaction modeling, and stability-aware feature gating. The low-rank compressor reduces model complexity under the $D\gg N$ setting, while progressive representation learning and residual projections improve optimization stability. A sparsity- and consistency-regularized feature-gating mechanism is introduced to reduce dependence on unstable latent representations. For biological interpretation, fold-averaged Integrated Gradients are employed to identify important transcriptomic probes, followed by gene-stability analysis and functional enrichment analysis.

Results: Experiments were conducted on a pediatric brain tumor gene expression dataset containing 130 samples and 54,676 probe-level features. Under repeated stratified 5-fold cross-validation with 10 repetitions, SA-FGPN achieved an accuracy of $0.9847\pm 0.0142$ , macro-F1 of $0.9728\pm 0.0201$ , and macro-AUC of $0.9916\pm 0.0078$ . Across 30 repeated random stratified splits, the maximum observed accuracy was 0.9897. SA-FGPN consistently outperformed classical machine learning methods and recent deep learning baselines, including autoencoder-, CNN-, CNN-BiLSTM-, and transformer-based approaches. Statistical significance testing, calibration analysis, permutation testing, ablation experiments, computational evaluation, and gene-stability analysis further demonstrated the robustness and reliability of the proposed framework.

Conclusion: SA-FGPN provides an effective, robust, and biologically interpretable solution for pediatric brain tumor classification from high-dimensional transcriptomic data. By integrating stability-aware feature selection with progressive representation learning, the proposed framework improves predictive performance while enabling the identification of biologically meaningful molecular signatures.