发表机构
University of Georgia; King Fahd University of Petroleum and Minerals（佐治亚大学; 法赫德国王石油与矿产大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出CANN框架，结合持久交换代数描述符与神经编码器，实现免比对的病毒家族分类，并在NCBI基因组集合上验证了其泛化性能。
AI 中文摘要
病毒基因组的广泛多样性对免比对表示方法提出了挑战，要求其既能捕获核苷酸组成，又能捕获重复序列模式的高阶组织。传统的$k$-mer表示方法量化了模式的丰度，但并未显式编码这些模式在多个尺度上的组织方式。在此，我们提出CANN，一个将持久交换代数描述符与神经表示学习相结合的免比对框架，用于病毒家族分类。CANN使用持久面描述符来编码$k$-mer模式的多尺度组织，并使用互补的$k$-mer计数来捕获序列组成。卷积和Transformer编码器分别独立学习这些特征的潜在表示，其各自嵌入空间中的余弦距离在$k$-近邻分类之前进行平均。在多个NCBI病毒基因组集合中，CANN实现了稳健的家族级性能，且编码器特定距离的融合相对于任一单独编码器持续改善了分类效果。对2026年NCBI集合中存在但2024年参考集中不存在的基因组的评估进一步支持了对参考数据中已代表家族的新成员的泛化能力。这些发现共同表明，持久交换代数描述符可以补充病毒基因组的神经表示，将基因组组织的代数描述与数据驱动的分类联系起来。
英文摘要
The extensive diversity of viral genomes challenges alignment-free representations to capture both nucleotide composition and the higher-order organization of recurring sequence patterns. Conventional $k$-mer representations quantify pattern abundance but do not explicitly encode how these patterns are organized across scales. Here, we introduce CANN, an alignment-free framework that combines persistent commutative-algebraic descriptors with neural representation learning for viral family classification. CANN uses persistent facet descriptors to encode the multiscale organization of $k$-mer patterns and complementary $k$-mer counts to capture sequence composition. Convolutional and Transformer encoders independently learn latent representations of these features, and cosine distances within their respective embedding spaces are averaged before $k$-nearest-neighbor classification. Across multiple NCBI viral genome collections, CANN achieves robust family-level performance, with the fusion of encoder-specific distances consistently improving classification relative to either encoder alone. Evaluation on genomes present in the 2026 NCBI collection but absent from the 2024 reference set further supports generalization to unseen members of families already represented in the reference data. Together, these findings show that persistent commutative-algebraic descriptors can complement neural representations of viral genomes, linking algebraic descriptions of genomic organization with data-driven classification.
