人工智能驱动的图方法在微生物组研究中的应用：一项系统综述。
完整摘要 · 中文译文
微生物组研究中高通量测序数据的复杂性，要求采用先进的分析方法来解析微生物群落结构及其相互作用。基于图的方法将分类单元建模为节点、将其关系建模为边，已成为实现这一目标的有力框架。然而，这些技术从经典网络分析到现代图表示学习的快速演进，造就了一个碎片化且复杂的方法学格局。本系统综述对该领域进行了全面的综合与结构化概述。我们系统回顾了相关文献，并提出三部分分类法，以归类每项研究的主要贡献：(1) 微生物网络表示的新方法，(2) 经典图分析的应用，(3) 图表示学习在预测建模中的新兴应用。我们总结了每一类别中最常见的应用，包括关键种识别、群落聚类和基于图的疾病分类。此外，我们识别了关键的跨领域挑战，例如缺乏标准化工具以及改善模型可解释性的需求。通过梳理基于图的方法的当前格局，本综述为研究人员选择合适的模型提供了重要指南，并突出了指导未来研究的关键空白。
完整摘要 · 英文原文
The complexity of high-throughput sequencing data in microbiome research necessitates advanced analytical methods to decipher microbial community structures and interactions. Graph-based methods, which model taxa as nodes and their relationships as edges, have emerged as a powerful framework for this purpose. However, the rapid evolution of these techniques, from classical network analysis to modern graph representation learning, has created a fragmented and complex methodological landscape. This systematic review provides a comprehensive synthesis and structured overview of the field. We systematically review the literature and propose a three-part classification to categorize the primary contribution of each study: (1) novel methods for microbial network representation, (2) applications of classical graph analysis, and (3) the emerging use of graph representation learning for predictive modeling. We summarize the most frequent applications within each class, including keystone species identification, community clustering, and graph-based disease classification. Furthermore, we identify critical cross-cutting challenges, such as the lack of standardized tools and the need for improved model interpretability. By structuring the current landscape of graph-based methods, this review serves as a crucial guide for researchers to select appropriate models and highlights key gaps to direct future research.