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

2025-12-10

AI Summary

To address feature sparsity, high computational complexity, and the limited performance of deep models on tabular biological data in molecular sequence classification, this paper proposes a novel sequence-to-image transformation framework. It integrates Chaos Game Representation (CGR) with algebraic-topological Rips complexes to generate topological images that jointly encode local sequence patterns and global topological structure. This work is the first to introduce the Rips complex into sequence visualization, providing theoretical guarantees of representation uniqueness, topological stability, and information preservation. Evaluated on anticancer peptide datasets for breast and lung cancer, the method achieves 86.8% and 94.5% classification accuracy, respectively—outperforming conventional vector-based approaches, sequence language models, and state-of-the-art image-based baselines. The framework is compatible with vision-oriented architectures such as Vision Transformers and ResNet, demonstrating both efficacy and generalizability in biomedical sequence analysis.

Technology Category

• Computer Vision: Medical and Biological Imaging
• Machine Learning: Representation Learning
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Data transparency and provenance

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.

Problem

• Transforms molecular sequences into topological images for classification
• Addresses sparsity and computational issues in traditional sequence analysis
• Enables vision-based deep learning on biological sequence data

Innovation

• Combines Chaos Game Representation with Rips complex construction
• Transforms molecular sequences into topological image features
• Enables vision-based deep learning for sequence analysis