Abstract
Deep-sea cold seep stage assessment has traditionally relied on costly, high-risk manned submersible operations and visual surveys of macrofauna. Although microbial communities provide a promising and more cost-effective alternative, reliable inference remains challenging because the available deep-sea dataset is extremely small ($n = 13$) relative to the microbial feature dimension ($p = 26$), making purely data-driven models highly prone to overfitting. To address this, we propose a knowledge-enhanced classification framework that incorporates an ecological knowledge graph as a structural prior. By fusing macro-microbe coupling and microbial co-occurrence patterns, the framework internalizes established ecological logic into a \underline{\textbf{G}}raph-\underline{\textbf{R}}egularized \underline{\textbf{M}}ultinomial \underline{\textbf{L}}ogistic \underline{\textbf{R}}egression (GRMLR) model, effectively constraining the feature space through a manifold penalty to ensure biologically consistent classification. Importantly, the framework removes the need for macrofauna observations at inference time: macro-microbe associations are used only to guide training, whereas prediction relies solely on microbial abundance profiles. Experimental results demonstrate that our approach significantly outperforms standard baselines, highlighting its potential as a robust and scalable framework for deep-sea ecological assessment.
Abstract (translated)
深海冷泉阶段评估传统上依赖成本高昂、高风险的有载人潜水器作业及大型动物视觉调查。尽管微生物群落提供了一种前景广阔且更具成本效益的替代方案，但由于现有深海数据集规模极小（n=13）而微生物特征维度较高（p=26），纯数据驱动模型极易出现过拟合，导致可靠推断仍具挑战性。为此，我们提出一种融合生态知识图谱作为结构先验的知识增强分类框架。该框架通过整合宏生物-微生物耦合关系与微生物共现模式，将既定生态逻辑内化至图正则多项逻辑回归（GRMLR）模型中，借助流形惩罚有效约束特征空间，确保分类结果符合生物学一致性。重要的是，该框架在推理阶段无需宏生物观测数据：宏生物-微生物关联仅用于指导训练，而预测完全依赖微生物丰度谱。实验结果表明，本方法显著优于标准基线方法，凸显其作为稳健可扩展的深海生态评估框架的潜力。
URL
https://arxiv.org/abs/2603.23961
