Explicit Path CGR: Maintaining Sequence Fidelity in Geometric Representations
This addresses a fundamental limitation in geometric representations for bioinformatics, enabling both accurate analysis and complete sequence recovery where both are essential.
The paper tackles the problem of sequence information loss in traditional Chaos Game Representation (CGR) methods for biological sequence analysis by introducing Reverse-CGR (R-CGR), which enables perfect sequence reconstruction through explicit path encoding and rational arithmetic precision control. The method achieves competitive performance on biological sequence classification tasks while providing interpretable geometric visualizations.
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.