Set-Aggregated Genome Embeddings for Microbiome Abundance Prediction

• URL: http://arxiv.org/abs/2605.12286v1
• Date: Tue, 12 May 2026 15:44:29 GMT
• Title: Set-Aggregated Genome Embeddings for Microbiome Abundance Prediction
• Abstract summary: We employ set-aggregated genome embeddings to predict community-level abundance profiles.<n>We benchmark this approach to show improved generalization on novel genomes.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Microbiome functions are encoded within the genes of the community-wide metagenome. A natural question is whether properties of a microbial community can be predicted just from knowing the raw DNA sequences of its members. In this work, we employ set-aggregated genome embeddings (SAGE) to predict community-level abundance profiles, exploiting the few-shot learning capabilities of genomic language models (GLMs). We benchmark this approach to show improved generalization on novel genomes compared to classical bioinformatics approaches. Model ablation shows that community-level latent representations directly result in improved performance. Lastly, we demonstrate the benefits of intermediate transformations between latent representations and demonstrate the differences between GLM embedding choices.

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