用符号积分梯度解析微生物组模型中物种与丰度的因果作用
Beyond Attention: Signed Integrated Gradients Attribution in a BiomeGPT-Style Microbiome Transformer
- 基于物种-丰度解耦的基线设计，分离输入源对输出的影响
- 揭示病原与保护性微生物的正负信号方向，突破注意力权重非负限制
- 可诊断微生物群落互作规则，适合研究疾病机制的生物学家
在如BiomeGPT这类特征标记化Transformer模型中，每个输入标记由固定物种（S）与可变丰度（A）融合而成：T = S + A。现有方法依赖[CLS]标记的注意力权重进行解释，但其存在两大缺陷：仅输出非负值，无法区分致病与保护性证据；且在标记融合后作用，掩盖了物种与丰度各自的影响。为此，本文采用符号积分梯度（Integrated Gradients）这一融合感知的归因方法，提出源导出基线T' = S + A₀，保持物种身份不变，仅隔离丰度变化的影响。应用于疾病-健康分类边界时，该方法能明确区分致病与保护性微生物信号。结果揭示了物种-丰度间的定向关系及敏感性诊断，这些信息被传统注意力方法完全遮蔽。进一步建议使用二阶积分海森矩阵（Integrated Hessians），以发现微生物群落中成员扰动如何改变模型对其他物种的敏感性，识别在特定丰度下推动模糊案例向疾病或健康倾斜的关键物种。该方法为BiomeGPT等平滑可微的特征标记化Transformer提供可解释性框架，具备通用性。代码已开源。
原文摘要 · Abstract (English)
In a feature-tokenized transformer (arXiv:2106.11959) such as BiomeGPT (doi:10.64898/2026.01.05.697599), each input token is built by fusing a fixed identity with a sample-specific measurement: a fixed species and a variable abundance, T = S + A. To interpret downstream classification in such models, prior work inspects the attention weights of the special [CLS] token (arXiv:2106.11959, arXiv:1810.04805, BiomeGPT) to rank sample tokens by importance. These weights have two critical limitations: they are nonnegative, so they cannot separate disease-supporting from health-supporting evidence (arXiv:2201.12114), and they act after token fusion, obscuring how the input sources S and A each affect the output. To address this we use Integrated Gradients (arXiv:1703.01365), a signed, fusion-aware attribution method, and propose a source-derived baseline T' = S + A_0 for feature-tokenized models such as BiomeGPT, which preserves species identity as a fixed biological coordinate while isolating the effect of abundance variation. Applied to a disease-versus-health decision margin, it yields polarity that explicitly separates pathogenic from protective microbial signals. We show that this gradient-based approach uncovers species-abundance directional relationships and sensitivity diagnostics entirely obscured by unsigned [CLS] attention weights. We further recommend second-order Integrated Hessians (arXiv:2002.04138) to expose microbiome community interaction rules: how a perturbation in one member alters the model's sensitivity to another, and which other species drive ambiguous cases toward disease or health at a given abundance level. This provides a principled approach to explainability in BiomeGPT that generalizes to other smooth and differentiable feature-tokenized transformers. Code is available at https://github.com/nohren/token-source-attribution
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