Structured Gaussian Processes for Uncertainty-Aware Classification of High-Dimensional, Small-Sampled Omics Data

2026-07-02

AI Summary

This work addresses the challenge of heterogeneous classification in high-dimensional, small-sample omics data, where nonlinear interactions and class imbalance—particularly the underrepresentation of certain phenotypes—hinder reliable prediction. To enhance the reliability of minority-class predictions, we propose a structured Gaussian process classification framework that uniquely integrates biological pathway graphs directly into the kernel function. This approach jointly models microbial abundance and network topology, while incorporating resampling, threshold calibration, and confusion matrix adjustment strategies to mitigate class imbalance. Evaluated on three gut microbiome datasets, the model significantly outperforms unstructured baselines, achieving state-of-the-art performance in minority-class recognition and improved calibration of predictive uncertainty.

Abstract

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.

Problem

• omics data
• high-dimensional
• small-sample
• class imbalance
• nonlinear interactions

Innovation

• Structured Gaussian Process
• Biological Pathway Integration
• Uncertainty Quantification
• High-Dimensional Small-Sample Classification
• Kernel Design