用拓扑方法把分子序列转成图像，提升抗癌肽分类效果
Sequence-to-Image Transformation for Sequence Classification Using Rips Complex Construction and Chaos Game Representation
- 结合分形图和代数拓扑构造图像表示
- 乳腺癌和肺癌数据集准确率分别达86.8%和94.5%
- 适合用视觉模型分析生物序列的研究者
传统分子序列分类的特征工程面临稀疏性和计算复杂性问题，而深度学习在表格型生物数据上表现不佳。本文提出一种新颖的拓扑方法，通过混沌游戏表示（CGR）与代数拓扑中的Rips复形构造，将分子序列转换为图像。该方法将序列元素映射至二维坐标，计算两两距离并构建Rips复形，以捕捉局部结构与全局拓扑特征。我们提供了表示唯一性、拓扑稳定性及信息保留性的形式化保证。在抗癌肽数据集上的大量实验表明，该方法优于基于向量、序列语言模型及现有图像方法，在乳腺癌和肺癌数据集上分别达到86.8%和94.5%的准确率。拓扑表示有效保留关键序列信息，同时可兼容基于视觉的深度学习架构用于分子序列分析。
原文摘要 · Abstract (English)
Traditional feature engineering approaches for molecular sequence classification suffer from sparsity issues and computational complexity, while deep learning models often underperform on tabular biological data. This paper introduces a novel topological approach that transforms molecular sequences into images by combining Chaos Game Representation (CGR) with Rips complex construction from algebraic topology. Our method maps sequence elements to 2D coordinates via CGR, computes pairwise distances, and constructs Rips complexes to capture both local structural and global topological features. We provide formal guarantees on representation uniqueness, topological stability, and information preservation. Extensive experiments on anticancer peptide datasets demonstrate superior performance over vector-based, sequence language models, and existing image-based methods, achieving 86.8\% and 94.5\% accuracy on breast and lung cancer datasets, respectively. The topological representation preserves critical sequence information while enabling effective utilization of vision-based deep learning architectures for molecular sequence analysis.
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