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

Junlan Liu, Qian Tang, Yongqi Han, Jinxing Song, Fei Wang, Pei Guo (), Chunhai Fan (), Weihong Tan () and Da Han ()
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Junlan Liu: Shanghai Jiao Tong University, Institute of Molecular Medicine (IMM), Renji Hospital, School of Medicine
Qian Tang: Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM)
Yongqi Han: Shanghai Jiao Tong University, Institute of Molecular Medicine (IMM), Renji Hospital, School of Medicine
Jinxing Song: Shanghai Jiao Tong University, Institute of Molecular Medicine (IMM), Renji Hospital, School of Medicine
Fei Wang: Shanghai Jiao Tong University, School of Chemistry and Chemical Engineering, New Cornerstone Science Laboratory, Frontiers Science Center for Transformative Molecules, National Center for Translational Medicine
Pei Guo: Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM)
Chunhai Fan: Shanghai Jiao Tong University, Institute of Molecular Medicine (IMM), Renji Hospital, School of Medicine
Weihong Tan: Shanghai Jiao Tong University, Institute of Molecular Medicine (IMM), Renji Hospital, School of Medicine
Da Han: Shanghai Jiao Tong University, Institute of Molecular Medicine (IMM), Renji Hospital, School of Medicine

Nature Communications, 2025, vol. 16, issue 1, 1-10

Abstract: Abstract 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.

Date: 2025
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DOI: 10.1038/s41467-025-66610-1

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