用双曲空间分析基因序列，提升分类准确率。
Breaking the Euclidean Barrier: Hyperboloid-Based Biological Sequence Analysis
- 将序列特征映射到双曲面空间，保留层级结构
- 通过双曲内积计算相似性，提升分类性能
- 适合处理具有层次关系的生物序列数据
基因序列分析在多个科学与医学领域至关重要。传统机器学习方法在高维欧氏空间中难以捕捉序列数据的复杂关系和层级结构，限制了序列分类与相似性度量的准确性。为此，本文提出一种将生物序列特征表示转换至双曲面空间的方法。通过该变换，序列被映射到双曲面，保留其内在结构信息。在双曲面空间中，基于双曲特征计算核矩阵，捕获序列间的成对相似性，利用双曲特征向量的内积度量序列对之间的相似性。实验评估表明，该方法能有效捕捉重要序列关联，显著提升分类准确率。
原文摘要 · Abstract (English)
Genomic sequence analysis plays a crucial role in various scientific and medical domains. Traditional machine-learning approaches often struggle to capture the complex relationships and hierarchical structures of sequence data when working in high-dimensional Euclidean spaces. This limitation hinders accurate sequence classification and similarity measurement. To address these challenges, this research proposes a method to transform the feature representation of biological sequences into the hyperboloid space. By applying a transformation, the sequences are mapped onto the hyperboloid, preserving their inherent structural information. Once the sequences are represented in the hyperboloid space, a kernel matrix is computed based on the hyperboloid features. The kernel matrix captures the pairwise similarities between sequences, enabling more effective analysis of biological sequence relationships. This approach leverages the inner product of the hyperboloid feature vectors to measure the similarity between pairs of sequences. The experimental evaluation of the proposed approach demonstrates its efficacy in capturing important sequence correlations and improving classification accuracy.
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