用基因组嵌入预测微生物群落丰度，提升新基因组泛化能力。
Set-Aggregated Genome Embeddings for Microbiome Abundance Prediction
- 通过集合聚合基因组嵌入，利用基因组语言模型少样本学习能力。
- 在新基因组上表现优于传统生物信息学方法，提升泛化性。
- 揭示潜在表征对性能的关键作用，适合微生物组研究者参考。
微生物群落的功能编码在其全体基因组的宏基因组中。一个自然问题是：仅凭成员的原始DNA序列，能否预测群落水平的丰度特征？本文采用集合聚合基因组嵌入（SAGE）方法，利用基因组语言模型（GLMs）的少样本学习能力，预测社区级丰度谱。实验表明，该方法在新基因组上的泛化性能优于经典生物信息学方法。模型消融分析显示，群落级潜在表征直接提升了性能。最后，我们展示了潜在表征间中间变换的益处，并对比了不同GLM嵌入选择的差异。
原文摘要 · Abstract (English)
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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