Enzymatic Weight Update Algorithm for DNA-Based Molecular Learning
Abstract
Recent research in DNA nanotechnology has demonstrated that biological substrates can be used for computing at a molecular level. However, in vitro demonstrations of DNA computations use preprogrammed, rule-based methods which lack the adaptability that may be essential in developing molecular systems that function in dynamic environments. Here, we introduce an in vitro molecular algorithm that ‘learns’ molecular models from training data, opening the possibility of ‘machine learning’ in wet molecular systems. Our algorithm enables enzymatic weight update by targeting internal loop structures in DNA and ensemble learning, based on the hypernetwork model. This novel approach allows massively parallel processing of DNA with enzymes for specific structural selection for learning in an iterative manner. We also introduce an intuitive method of DNA data construction to dramatically reduce the number of unique DNA sequences needed to cover the large search space of feature sets. By combining molecular computing and machine learning the proposed algorithm makes a step closer to developing molecular computing technologies for future access to more intelligent molecular systems.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Apr 10, 2019
- Source ID
- 10.3390/molecules24071409
Entities
People
- Beom-jin Lee
- Byoung-tak Zhang
- Christina Baek
- Dong-hyun Kwak
- Sang-woo Lee
Organizations
- Agency for Defense Development
- Air Force Office of Scientific Research
- Institute for Information and Communications Technology Planning and Evaluation
- Korea Planning & Evaluation Institute of Industrial Technology