发表机构
University of Georgia（佐治亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
GrassTop结合格拉斯曼流形与代数拓扑，通过多尺度k-mer描述符和低秩子空间距离实现病毒分类与系统发育分析，在多个数据集上优于五种无比对方法，UPGMA树标签纯度达1.0。
AI 中文摘要
我们提出了GrassTop，一种整合格拉斯曼流形和代数拓扑的基因组表示方法，用于病毒分类和系统发育分析。该框架首先构建k-mer位置模式的多尺度拓扑和谱描述符，然后提取一个低秩子空间来总结跨过滤的变化，并使用格拉斯曼距离比较基因组。尽管所报告的实现使用弦距离，但该框架并不局限于这一特定选择。我们在四个病毒分类数据集家族、四个系统发育聚类数据集以及一个序列扰动实验上评估了GrassTop。在所报告的5-最近邻协议下，GrassTop在所有报告的分类指标和数据集上均取得了高于五种已发表的无比对参考方法的得分。其UPGMA（非加权配对算术平均法）树在每个系统发育数据集上实现了平均标签纯度为1.0。扰动实验提供了更细致的结果：对于SARS-CoV-2，子空间表示与未投影特征矩阵的直接比较差异最为明显，而对于其他数据集，差异较小或非单调。总体而言，这些结果支持GrassTop作为病毒分类和系统发育分析的有效拓扑-几何表示方法。
英文摘要
We introduce GrassTop, a genome representation that integrates Grassmann manifolds and algebraic topology for viral classification and phylogenetic analysis. The framework begins by constructing multiscale topological and spectral descriptors of (k)-mer positional patterns. It then extracts a low-rank subspace that summarizes variation across the filtration and compares genomes using a Grassmannian distance. Although the reported implementation uses the chordal distance, the framework is not restricted to this particular choice. We evaluate GrassTop on four families of viral classification datasets, four phylogenetic clustering datasets, and a sequence perturbation experiment. Under the reported 5-nearest-neighbor protocol, GrassTop achieves higher scores than five published alignment-free reference methods across all reported classification metrics and datasets. Its UPGMA (unweighted pair-group method using arithmetic averages) trees achieve an average label purity of 1.0 on every phylogenetic dataset. The perturbation experiment provides a more nuanced result: the subspace representation differs most clearly from direct comparison of the unprojected feature matrices for SARS-CoV-2, whereas the differences are smaller or non-monotonic for the other datasets. Overall, these results support GrassTop as an effective topological-geometric representation for viral classification and phylogenetic analysis.
Comments19 pages
