Interpretable DNA Sequence Classification via Dynamic Feature Generation in Decision Trees

arXiv:2604.12060v1 Announce Type: cross Abstract: 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: consideri