GenEx：基于图的密码子共现网络的SARS-CoV-2变异株检测表示范式

中文摘要 AI 辅助

对SARS-CoV-2变异株（Beta、Gamma、Delta、Omicron）等病毒的基因组分析，目前主要依赖序列比对、系统发育分析和突变频率统计等经典生物信息学方法。这些方法采用成对密码子或核苷酸距离矩阵分析基因序列，将其视为线性字符串，无法捕捉复杂的上下文相互依赖关系。我们提出了GenEx，这一 pipeline 可将原始基因序列转换为密码子共现图，并提取25种以上图特征。我们在图生成和特征提取方面的两项核心技术为MSCG（多尺度密码子共现图）和LAPCG（线性时间邻域PMI密码子图）。通过这些算法，我们将密码子序列视为可被密码子共现图分析解释的结构化符号词汇，这一表示范式借鉴自计算语言学。另一项主要贡献是采用奇异值分解（SVD）实现谱图特征提取，使用平方奇异值（σ²）替代传统使用的特征值，这有助于放大主导与次主导谱分量之间的分离度，从而增强下游分类中的类间可分性。为进一步验证方法有效性，我们针对最新SARS-CoV-2变异株训练了23个基准机器学习模型，在所有SARS-CoV-2变异株检测中取得了显著结果。

英文摘要

Genomic analysis on viruses such as SARS-CoV-2 variants: Beta, Gamma, Delta, and Omicron is heavily dominated by classical bioinformatics methods, including Sequence Alignment, Phylogenetic Analysis, and Mutation Frequency Statistics. These approaches use pairwise codon or nucleotide distance matrices to analyze gene sequences, treating them as linear strings rather than capturing their complex contextual interdependencies. We proposed GenEx, a pipeline that converts raw gene sequences into codon co-occurrence graphs and extracts more than 25 graph features. Our two most prominent techniques for graph generation and feature extraction are MSCG (Multi-Scale Codon Co-occurrence Graph) and LAPCG (Linear-time Adjacency PMI Codon Graph). Using these algorithms, we treated codon sequences as structured symbolic vocabularies interpretable to codon co-occurrence graph analysis, a representational paradigm borrowed from computational linguistics. Another major contribution includes implementing a spectral graph feature extraction using Singular Value Decomposition (SVD), using the squared singular value ($σ^2$) instead of the traditionally used eigenvalue, which helped us to amplify the separation between dominant and subdominant spectral components, thereby enhancing inter-class separability in downstream classification. And to further demonstrate that our method works, we trained 23 benchmarked ML models against the latest SARS-CoV-2 variants, achieving remarkable results in detecting all SARS-CoV-2 variants.

发表机构

• North South University（北南大学）

机构由 AI 辅助整理，请以论文原文为准。