Sequence-to-Image Transformation for Sequence Classification Using Rips Complex Construction and Chaos Game Representation

TL;DR

This paper presents a novel topological method combining Chaos Game Representation and Rips complex construction to transform molecular sequences into images, enabling improved classification performance with vision-based deep learning models.

Contribution

It introduces a new topological approach for sequence-to-image transformation that guarantees uniqueness, stability, and preserves information, outperforming existing methods.

Findings

Achieved 86.8% accuracy on breast cancer dataset

Achieved 94.5% accuracy on lung cancer dataset

Outperformed vector-based and sequence language models

Abstract

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.