Interpretable DNA Sequence Classification via Dynamic Feature Generation in Decision Trees
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 sp
Record details
Published: 13 April 2026
Source: arXiv
Category: Research
Topics: Regulation · Biotech
Retrieved: 14 July 2026
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ethics.ai (13 April 2026), “Interpretable DNA Sequence Classification via Dynamic Feature Generation in Decision Trees,” evidence record 5914, https://ethics.ai/record/5914 (originally published by arXiv).
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