让微生物组样本嵌入更关注物种丰度，提升分类准确率。
Abundance-Aware Set Transformer for Microbiome Sample Embedding
- 按物种丰度加权序列嵌入，再用自注意力聚合
- 在真实数据上分类效果接近完美，优于平均池化
- 适合需要生物可解释性的微生物组研究者
将微生物组样本表示为可输入大语言模型的嵌入向量，对表型预测和环境分类等下游任务至关重要。以往方法多采用简单平均法处理序列嵌入，常忽略物种丰度的生物学意义。本文提出一种丰度感知的集合变换器，通过按相对丰度加权序列嵌入，并复制相应数量的嵌入向量后进行自注意力聚合，构建固定长度的样本级嵌入。该方法不改变模型结构，在真实微生物组分类任务中表现优于平均池化与无权重集合变换器，在部分任务中达到完美性能。结果表明，丰度感知聚合能生成更鲁棒且具生物学意义的微生物组表示。据我们所知，这是首个将序列级丰度信息整合进基于Transformer的样本嵌入的方法。
原文摘要 · Abstract (English)
Microbiome sample representation to input into LLMs is essential for downstream tasks such as phenotype prediction and environmental classification. While prior studies have explored embedding-based representations of each microbiome sample, most rely on simple averaging over sequence embeddings, often overlooking the biological importance of taxa abundance. In this work, we propose an abundance-aware variant of the Set Transformer to construct fixed-size sample-level embeddings by weighting sequence embeddings according to their relative abundance. Without modifying the model architecture, we replicate embedding vectors proportional to their abundance and apply self-attention-based aggregation. Our method outperforms average pooling and unweighted Set Transformers on real-world microbiome classification tasks, achieving perfect performance in some cases. These results demonstrate the utility of abundance-aware aggregation for robust and biologically informed microbiome representation. To the best of our knowledge, this is one of the first approaches to integrate sequence-level abundance into Transformer-based sample embeddings.
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