Breast Tumor Diagnosis Based on Molecular Learning Vector Quantization Neural Networks.

ABSTRACT: DNA nanotechnology plays a crucial role in precise cancer medicine. Currently, molecular logic circuits are applied to detect tumor-specific biomarkers and control the release of therapeutic drugs. However, these systems lack self-learning capabilities for intelligent diagnostics in biological samples, and their data processing capabilities are limited. Here, a molecular learning vector quantization neural network (LVQNN) model based on DNA strand displacement (DSD) technology for breast tumor diagnosis is developed. Compared to previous work, the molecular LVQNN boasts powerful computing abilities, handling high-dimensional data for intelligent cancer diagnosis. To verify the feasibility and versatility of the network, two distinct typical datasets are selected: one from a single source w

SUBMITTER: Huang C 

PROVIDER: S-EPMC11558146 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature