{Reference Type}: Journal Article
{Title}: A spatially localized DNA linear classifier for cancer diagnosis.
{Author}: Yang L;Tang Q;Zhang M;Tian Y;Chen X;Xu R;Ma Q;Guo P;Zhang C;Han D;
{Journal}: Nat Commun
{Volume}: 15
{Issue}: 1
{Year}: 2024 May 29
{Factor}: 17.694
{DOI}: 10.1038/s41467-024-48869-y
{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.