新方法让生物序列几何表示可完全还原，兼顾精度与可解释性。
Explicit Path CGR: Maintaining Sequence Fidelity in Geometric Representations
- 通过显式路径编码和有理数精度控制，实现序列完全恢复
- 在序列分类任务中表现媲美传统方法，且能完美重建原始序列
- 适合需要可解释性的生物信息学分析场景
我们提出一种新型信息保全的混沌游戏表示（CGR）方法，称为逆向CGR（R-CGR），用于解决传统CGR在几何映射中丢失序列信息的根本问题。该方法通过显式路径编码与有理数精度控制，实现从存储的几何轨迹中完全恢复原始序列。与纯几何方法不同，R-CGR通过完整路径存储，保留每一步的位置与字符信息。我们在生物序列分类任务上验证了其有效性，性能可与传统序列方法竞争，同时提供可解释的几何可视化。该方法生成的图像富含特征，适用于深度学习，且通过显式编码保持完整序列信息，为既需准确又需序列恢复的可解释生物信息学分析开辟新途径。
原文摘要 · Abstract (English)
We present a novel information-preserving Chaos Game Representation (CGR) method, also called Reverse-CGR (R-CGR), for biological sequence analysis that addresses the fundamental limitation of traditional CGR approaches - the loss of sequence information during geometric mapping. Our method introduces complete sequence recovery through explicit path encoding combined with rational arithmetic precision control, enabling perfect sequence reconstruction from stored geometric traces. Unlike purely geometric approaches, our reversibility is achieved through comprehensive path storage that maintains both positional and character information at each step. We demonstrate the effectiveness of R-CGR on biological sequence classification tasks, achieving competitive performance compared to traditional sequence-based methods while providing interpretable geometric visualizations. The approach generates feature-rich images suitable for deep learning while maintaining complete sequence information through explicit encoding, opening new avenues for interpretable bioinformatics analysis where both accuracy and sequence recovery are essential.
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