标题： 大规模流感突变数据集的k均值聚类降维 显示英文标题

标题： Dimensionality reduction for k-means clustering of large-scale influenza mutation datasets

摘要： 病毒突变通过增加传染性、增强疫苗抗性和改变疾病严重程度，对公共健康构成重大威胁。 为了追踪这些不断演变的模式，像疾控中心（CDC）这样的机构每年评估数千种病毒株，强调了深入理解病毒突变和进化的迫切需求。 在这项研究中，我们整合了基因组分析、聚类以及三种领先的降维方法，即主成分分析（PCA）、t分布随机邻域嵌入（t-SNE）和统一流形近似与投影（UMAP），以调查COVID-19对流感病毒传播的影响。 通过对广泛的疫情前和疫情后流感数据集应用这些方法，我们揭示了疫情期间的选择压力如何影响流感遗传的多样性。 我们的研究结果显示，结合强大的降维与聚类可以提供关于病毒突变复杂动态的关键见解，为未来的科研方向及公共卫生干预策略提供指导。 显示英文摘要

摘要： Viral mutations pose significant threats to public health by increasing infectivity, strengthening vaccine resistance, and altering disease severity. To track these evolving patterns, agencies like the CDC annually evaluate thousands of virus strains, underscoring the urgent need to understand viral mutagenesis and evolution in depth. In this study, we integrate genomic analysis, clustering, and three leading dimensionality reduction approaches, namely, principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and uniform manifold approximation and projection (UMAP)-to investigate the effects of COVID-19 on influenza virus propagation. By applying these methods to extensive pre- and post-pandemic influenza datasets, we reveal how selective pressures during the pandemic have influenced the diversity of influenza genetics. Our findings indicate that combining robust dimension reduction with clustering yields critical insights into the complex dynamics of viral mutation, informing both future research directions and strategies for public health intervention.