Interpretable DNA Sequence Classification via Dynamic Feature Generation in Decision Trees
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Acceso en línea: | |
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| _version_ | 1866914470665650176 |
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| author | Huynh, Nicolas Kacprzyk, Krzysztof Sheridan, Ryan Bentley, David van der Schaar, Mihaela |
| author_facet | Huynh, Nicolas Kacprzyk, Krzysztof Sheridan, Ryan Bentley, David van der Schaar, Mihaela |
| contents | The analysis of DNA sequences has become critical in numerous fields, from evolutionary biology to understanding gene regulation and disease mechanisms. While deep neural networks can achieve remarkable predictive performance, they typically operate as black boxes. Contrasting these black boxes, axis-aligned decision trees offer a promising direction for interpretable DNA sequence analysis, yet they suffer from a fundamental limitation: considering individual raw features in isolation at each split limits their expressivity, which results in prohibitive tree depths that hinder both interpretability and generalization performance. We address this challenge by introducing DEFT, a novel framework that adaptively generates high-level sequence features during tree construction. DEFT leverages large language models to propose biologically-informed features tailored to the local sequence distributions at each node and to iteratively refine them with a reflection mechanism. Empirically, we demonstrate that DEFT discovers human-interpretable and highly predictive sequence features across a diverse range of genomic tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_12060 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Interpretable DNA Sequence Classification via Dynamic Feature Generation in Decision Trees Huynh, Nicolas Kacprzyk, Krzysztof Sheridan, Ryan Bentley, David van der Schaar, Mihaela Machine Learning Artificial Intelligence Genomics The analysis of DNA sequences has become critical in numerous fields, from evolutionary biology to understanding gene regulation and disease mechanisms. While deep neural networks can achieve remarkable predictive performance, they typically operate as black boxes. Contrasting these black boxes, axis-aligned decision trees offer a promising direction for interpretable DNA sequence analysis, yet they suffer from a fundamental limitation: considering individual raw features in isolation at each split limits their expressivity, which results in prohibitive tree depths that hinder both interpretability and generalization performance. We address this challenge by introducing DEFT, a novel framework that adaptively generates high-level sequence features during tree construction. DEFT leverages large language models to propose biologically-informed features tailored to the local sequence distributions at each node and to iteratively refine them with a reflection mechanism. Empirically, we demonstrate that DEFT discovers human-interpretable and highly predictive sequence features across a diverse range of genomic tasks. |
| title | Interpretable DNA Sequence Classification via Dynamic Feature Generation in Decision Trees |
| topic | Machine Learning Artificial Intelligence Genomics |
| url | https://arxiv.org/abs/2604.12060 |