A spatially localized DNA linear classifier for cancer diagnosis.
Linlin YangQian TangMingzhi ZhangYuan TianXiaoxing ChenRui XuQian MaPei GuoChao ZhangDa HanPublished in: Nature communications (2024)
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.
Keyphrases
- circulating tumor
- single molecule
- cell free
- healthcare
- squamous cell carcinoma
- computed tomography
- circulating tumor cells
- magnetic resonance imaging
- machine learning
- deep learning
- young adults
- magnetic resonance
- cell proliferation
- electronic health record
- lymph node metastasis
- big data
- cell cycle arrest
- current status
- neural network
- pi k akt