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This study introduces a structured Gaussian process classification framework that incorporates graph-encoded biological pathways into the kernel construction to enhance classification of high-dimensional, small-sampled omics data. By integrating information from known interaction networks with traditional abundance features, the approach effectively addresses class imbalance and improves minority-class performance. Benchmarking against three microbiome datasets reveals that this hybrid method not only outperforms unstructured baselines but also matches established benchmarks, while providing calibrated predictive uncertainty for better decision-making.
Integrating biological interaction networks into Gaussian processes boosts minority-class performance in omics classification, outperforming traditional methods.
Classifying heterogeneous omics data remains a fundamental challenge in computational biology, particularly in high-dimensional, small-sample settings where nonlinear interactions dominate and class imbalance further complicates reliable prediction of minority phenotypes. While traditional kernel methods rely on feature abundance, they fail to leverage the known interaction landscapes of biological systems. In this work, we propose a structured Gaussian process classification framework that integrates graph-encoded biological pathways directly into the kernel construction. By propagating information along known interaction networks and combining this with abundance-derived features, the resulting classifier captures both quantitative measurements and topological context. We benchmark our proposed methodology on three publicly available gut and fecal microbiome datasets. To address severe class imbalance, we evaluate complementary strategies, including data-level resampling, threshold calibration, and confusion-matrix-based adjustments, and report minority-class performance alongside accuracy. The hybrid approach yields a performance gain over unstructured baselines and matches the performance of established benchmarks for similar datasets. Furthermore, the probabilistic nature of the framework naturally provides calibrated predictive uncertainty, enabling robust differentiation between confident predictions and ambiguous samples.
