A spatially localized DNA linear classifier for cancer diagnosis

Linlin Yang, Qian Tang, Mingzhi Zhang, Yuan Tian, Xiaoxing Chen, Rui Xu, Qian Ma, Pei Guo (), Chao Zhang () and Da Han ()
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Linlin Yang: Chinese Academy of Sciences
Qian Tang: Chinese Academy of Sciences
Mingzhi Zhang: Shanghai Jiao Tong University
Yuan Tian: Shanghai Jiao Tong University
Xiaoxing Chen: Shanghai Jiao Tong University
Rui Xu: Intellinosis Biotech Co.Ltd.
Qian Ma: Intellinosis Biotech Co.Ltd.
Pei Guo: Chinese Academy of Sciences
Chao Zhang: Shanghai Jiao Tong University
Da Han: Chinese Academy of Sciences

Nature Communications, 2024, vol. 15, issue 1, 1-11

Abstract: Abstract Molecular computing is an emerging paradigm that plays an essential role in data storage, bio-computation, and clinical diagnosis with the future trends of more efficient computing scheme, higher modularity with scaled-up circuity and stronger tolerance of corrupted inputs in a complex environment. Towards these goals, we construct a spatially localized, DNA integrated circuits-based classifier (DNA IC-CLA) that can perform neuromorphic architecture-based computation at a molecular level for medical diagnosis. The DNA-based classifier employs a two-dimensional DNA origami as the framework and localized processing modules as the in-frame computing core to execute arithmetic operations (e.g. multiplication, addition, subtraction) for efficient linear classification of complex patterns of miRNA inputs. We demonstrate that the DNA IC-CLA enables accurate cancer diagnosis in a faster (about 3 h) and more effective manner in synthetic and clinical samples compared to those of the traditional freely diffusible DNA circuits. We believe that this all-in-one DNA-based classifier can exhibit more applications in biocomputing in cells and medical diagnostics.

Date: 2024
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DOI: 10.1038/s41467-024-48869-y

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