Set-Aggregated Genome Embeddings for Microbiome Abundance Prediction
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866911675617116160 |
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| author | Kim, Younhun Gerber, Georg K. Gibson, Travis E. |
| author_facet | Kim, Younhun Gerber, Georg K. Gibson, Travis E. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_12286 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Set-Aggregated Genome Embeddings for Microbiome Abundance Prediction Kim, Younhun Gerber, Georg K. Gibson, Travis E. Genomics Artificial Intelligence 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. |
| title | Set-Aggregated Genome Embeddings for Microbiome Abundance Prediction |
| topic | Genomics Artificial Intelligence |
| url | https://arxiv.org/abs/2605.12286 |