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A spatially localized DNA linear classifier for cancer diagnosis.
- Source :
- Nature Communications; 5/29/2024, Vol. 15 Issue 1, p1-11, 11p
- Publication Year :
- 2024
-
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. Molecular computing is an emerging paradigm with a crucial role in clinical diagnosis. Here, authors develop a spatially localized, DNA-integrated circuits-based classifier, DNA IC-CLA, which enables accurate cancer diagnosis for clinical samples in a faster and more effective manner. [ABSTRACT FROM AUTHOR]
- Subjects :
- CANCER diagnosis
DNA folding
DATA warehousing
DIAGNOSIS
Subjects
Details
- Language :
- English
- ISSN :
- 20411723
- Volume :
- 15
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Nature Communications
- Publication Type :
- Academic Journal
- Accession number :
- 177559572
- Full Text :
- https://doi.org/10.1038/s41467-024-48869-y