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<p>DNA computing has emerged as a transformative paradigm for tackling computational problems at the molecular level, yet existing approaches remain constrained in algorithmic interpretability, efficiency, and scalability. Here we present a DNA-based decision tree system that modularly embeds classification rules into DNA strand displacement reaction cascades for interpretable decision-making across various configurations. It supports cascaded networks exceeding 10 layers, parallel computation of 13 decision trees in a Random Forest involving 333 strands, and multimode operation (linear/nonlinear, binary/multi-class, single/tandem trees), while maintaining low leakage, rapid signal propagation, and minimal computational elements. Coupled with a DNA-methylation sensing module, it translates biomarker profiles into molecular instructions for tree traversal, reproduces in-silico predictions and enables accurate disease subtype classification. The decision&#xa0;tree system represents an interpretable, scalable, and memory-efficient DNA&#xa0;computing approach and will&#xa0;open new avenues for programming intelligent&#xa0;molecular machines with broad applicability.</p>

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Interpretable molecular decision-making with DNA-based scalable and memory-efficient tree computation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月21日

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  • Junlan Liu,
  • Qian Tang,
  • Yongqi Han,
  • Jinxing Song,
  • Fei Wang,
  • Pei Guo,
  • Chunhai Fan,
  • Weihong Tan,
  • Da Han

摘要

DNA computing has emerged as a transformative paradigm for tackling computational problems at the molecular level, yet existing approaches remain constrained in algorithmic interpretability, efficiency, and scalability. Here we present a DNA-based decision tree system that modularly embeds classification rules into DNA strand displacement reaction cascades for interpretable decision-making across various configurations. It supports cascaded networks exceeding 10 layers, parallel computation of 13 decision trees in a Random Forest involving 333 strands, and multimode operation (linear/nonlinear, binary/multi-class, single/tandem trees), while maintaining low leakage, rapid signal propagation, and minimal computational elements. Coupled with a DNA-methylation sensing module, it translates biomarker profiles into molecular instructions for tree traversal, reproduces in-silico predictions and enables accurate disease subtype classification. The decision tree system represents an interpretable, scalable, and memory-efficient DNA computing approach and will open new avenues for programming intelligent molecular machines with broad applicability.