Hierarchical Multi-Omics Trajectory Prediction for fecal microbiota transplantation: a novel machine learning framework for small-sample longitudinal multi-omics integration.

Abstract

Fecal microbiota transplantation (FMT) has emerged as a highly effective treatment for recurrent Clostridioides difficile infection and is being actively investigated for numerous other conditions. While multi-omics studies have revealed dynamic changes in microbial communities and host metabolism following FMT, existing approaches are primarily descriptive and lack the ability to model individual patient trajectories or identify early biomarkers of treatment response. Small-sample, multi-omics, longitudinal prediction presents unique computational challenges: high dimensionality ($p \gg n$), multi-omics integration, temporal dynamics, and interpretability. Here, we present Hierarchical Multi-Omics Trajectory Prediction (HMOTP), a purpose-built machine learning framework that addresses these challenges through hierarchical feature construction, multilevel attention mechanisms, and patient-specific trajectory prediction. We evaluated HMOTP on 15 patients with recurrent Clostridioides difficile infection who underwent FMT, with lipidomics and metagenomics profiling at four timepoints spanning 6 months. Notably, naively concatenating multi-omics features degraded Random Forest performance ($93.33\%$ to $87.18\%$ accuracy), whereas HMOTP's hierarchical integration benefited from the additional omics layer, demonstrating that its advantage stems from structure, not from access to more data. Through hierarchical interpretability, HMOTP identified key biomarkers and revealed cross-omics associations between host lipid metabolism and microbial energy pathways, demonstrating utility for longitudinal modeling and biological discovery in FMT response. HMOTP provides a generalizable, principled framework for personalized medicine applications across small-sample multi-omics problems. Source code and a demo dataset are publicly available.