Sequence-to-Image Transformation for Sequence Classification Using Rips Complex Construction and Chaos Game Representation
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912757329166336 |
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| author | Ali, Sarwan Murad, Taslim Khan, Imdadullah |
| author_facet | Ali, Sarwan Murad, Taslim Khan, Imdadullah |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_10141 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sequence-to-Image Transformation for Sequence Classification Using Rips Complex Construction and Chaos Game Representation Ali, Sarwan Murad, Taslim Khan, Imdadullah Machine Learning 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. |
| title | Sequence-to-Image Transformation for Sequence Classification Using Rips Complex Construction and Chaos Game Representation |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2512.10141 |