Understanding why only some breast cancers become metastatic, and predicting metastatic risks at earlier cancer stages, are two major goals of breast cancer research. These goals are clearly connected and synergistic, but are rarely integrated within a single study. Knowledge discovery-oriented research has identified critical biological pathways related to metastasis, but the interplay between these pathways remains elusive, resulting in models that lack prediction accuracy. Conversely, complex machine learning models achieve high prediction accuracy without detailed biological knowledge, making the explanation challenging. Here, we propose a novel computational framework that addresses both goals simultaneously. To support knowledge discovery, we construct gene regulatory networks (GRNs) to model the cellular states of metastatic and non-metastatic patients. To enable explainable metastasis prediction, we introduce a dysregulation score based on the GRN models. Experimental results demonstrate that our method not only identified significant metastasis-associated GRN changes, but also revealed the loss of co-regulation among key biological processes in metastatic patients. Leveraging the dysregulation score, our model-free classifier outperformed complex machine learning models under rigorous evaluation. This work bridges a significant gap between knowledge discovery and accurate, explainable prediction by employing carefully designed knowledge models and knowledge-based prediction, with potential applicability in other disease contexts.

Classification tasks in many real-world domains are exacerbated by class imbalance, relatively small sample sizes compared to high dimensionality, and measurement uncertainty. The problem of class imbalance has been extensively studied, and data augmentation methods based on interpolation of minority class instances have been proposed as a viable solution to mitigate imbalance. It remains to be seen whether augmentation can be applied to improve the overall performance while maintaining stability, especially with a limited number of samples. In this paper, we present a novel feature-space augmentation technique that can be applied to high-dimensional data for classification tasks and address these issues. Our method utilizes uniform random sampling and introduces synthetic instances by taking advantage of the local distributions of individual features in the observed instances. The core augmentation algorithm is class-invariant, which opens up an unexplored avenue of simultaneously improving and stabilizing performance by augmenting unlabeled instances. The proposed method is evaluated using a comprehensive performance analysis involving multiple classifiers and metrics. Comparative analysis with existing feature space augmentation methods strongly suggests that the proposed algorithm can result in improved classification performance while also increasing the overall reliability of the performance evaluation.