AI-Driven Microbial Diagnostics: Predicting Disease Signatures Through Microbial Pattern Recognition

Area of Science:

• Microbiome research
• Computational biology
• Machine learning for health

Background:

• Predicting diseases from gut microbiome data is challenging due to data complexity and limitations in current analytical models.
• Existing methods often fail to capture intricate inter-taxon interactions and the evolutionary structure within microbial communities.
• Compositional data issues and batch heterogeneity further complicate accurate disease prediction from microbiome profiles.

Purpose of the Study:

• Introduce DysbioFormer, a novel Dysbiosis-Aware Multiset Transformer Framework for disease prediction using gut microbiome patterns.
• Address the limitations of existing methods in modeling complex microbial community interactions and evolutionary structures.
• Develop a scalable and cohort-agnostic framework for accurate microbiome-based diagnostics.

Main Methods:

• Model gut microbiome samples as permutation-invariant multisets of taxonomic tokens, incorporating compositional, phylogenetic, and harmonized cohort data.
• Utilize Stacked Set Attention Blocks to learn relational dependencies between microbial taxa.
• Employ Pooling-by-Multihead-Attention for aggregating global disease-level embeddings without sequence assumptions.

Main Results:

• DysbioFormer achieved high diagnostic performance on the MicrobiomeHD dataset, with 97% accuracy, 0.97 AUC, and 96% F1-score.
• The framework consistently outperformed classical machine learning models under identical evaluation protocols.
• Attention-derived signatures provided interpretable links between predictions and disease-associated microbes, enhancing biological plausibility.

Conclusions:

• The DysbioFormer architecture enables scalable, cohort-agnostic microbial diagnostics, translating complex microbiome information into clinical insights.
• This framework establishes a foundation for future microbiome-based disease screening and precision health applications.
• The design supports extension to multi-omics integration, longitudinal studies, and decision-support systems for microbiome-informed translational medicine.