A nano instance-based learning by non-specific hybridization of DNA sequences

Abstract

Abstract DNA, or deoxyribonucleic acid, is a powerful molecule that plays a fundamental role in the storing and processing genetic information of all living organisms. In recent years, scientists over the world have devoted to taking advantage of its high density, energy efficiency and long durability to solve the challenges in information technology. Here, we propose to build an instance-based learning model by DNA molecules. The handwriting digit images in MNIST dataset are encoded by DNA sequences using a deep learning encoder. And the reversal complementary sequence of a query image is used to hybridize with the training instance sequences. Simulation results by NUPACK show that this classification model by DNA could achieve 95% accuracy on average. Wet-lab experiments also validate the predicted yield is consistent with the hybridization strength. Our work proves that it is feasible to build an effective instance-based classification model for practical application.