Learning from high-dimensional, low-sample-size (HDLSS) data remains a persistent challenge in machine learning, as models must infer reliable patterns from limited observations while handling an excessive number of variables—a scenario particularly prevalent in biomedical applications. Such data structures render predictive modeling highly vulnerable to erratic optimization and overfitting. To address this challenge, we propose the Global Interaction Network with Dynamic Generalization Control (GINet-DGC), an artificial intelligence (AI) framework that integrates feature-wise structural priors with dynamic generalization monitoring. Rather than directly learning an unconstrained first-layer weight matrix, GINet-DGC generates task-specific weights from multi-view feature descriptors, encompassing latent semantic, global distributional, local topological, and hierarchical representations. This structure-constrained weight generation strategy effectively narrows the feature-interaction search space and acts as an inductive regularizer against noise and redundant molecular features. Furthermore, we introduce an Overfitting-aware Index (OFI) to monitor the training trajectory and effectively identify the generalization saturation point for adaptive termination. Empirical evaluations on eight public real-world biomedical HDLSS gene-expression datasets, using a repeated stratified 5 × 5 cross-validation protocol, demonstrate that GINet-DGC achieves competitive and stable performance against 17 baselines. These findings support the effectiveness of the proposed framework within the evaluated public biomedical HDLSS benchmark setting.

Learning from High-Dimensional Low-Sample Size data has long been a persistent challenge in machine learning, because models must infer reliable patterns from limited observations while handling a large number of variables,this challenge is especially common in biomedical applications. High-dimensional small-sample data make predictive modeling vulnerable to unstable training and overfitting. To address this issue, we propose GINet-DGC, an AI framework that integrates feature-wise structural priors with dynamic generalization monitoring. Instead of directly learning an unconstrained first-layer weight matrix, GINet-DGC generates task-specific weights from multi-view feature descriptors, including latent semantic, global distributional, local topological, and hierarchical representations. This structure-constrained weight generation design narrows the feature-interaction search space and acts as an inductive regularizer against noise and redundant molecular features. Furthermore, we introduce an Overfitting-aware Index to monitor the training trajectory and detect the generalization saturation point for adaptive termination. Experiments on eight publicly accessible real-world biomedical HDLSS gene-expression datasets, evaluated under repeated stratified 5×5 cross-validation, show that GINet-DGC achieves competitive and stable performance against 17 baselines. These results suggest that explicitly encoding structural priors can improve robust AI modeling for data-scarce benchmarks.