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

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 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 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.