A molecular evolutionary algorithm for learning hypernetworks on simulated DNA computers
Jihoon Lee,Bado Lee,Joon Shik Kim,Russell Deaton,Byoung-Tak Zhang +4 more
- 05 Jun 2011
- pp 2735-2742
TL;DR: A “molecular” evolutionary algorithm that can be implemented in DNA computing in vitro to learn the recently-proposed hypernetwork model of cognitive memory, which provides unique properties that are distinguished from conventional evolutionary algorithms and makes a new addition to the arsenal of tools in evolutionary computation.
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Abstract: We describe a “molecular” evolutionary algorithm that can be implemented in DNA computing in vitro to learn the recently-proposed hypernetwork model of cognitive memory. The molecular learning process is designed to make it possible to perform wet-lab experiments using DNA molecules and bio-lab tools. We present the bio-experimental protocols for selection, amplification and mutation operators for evolving hypernetworks. We analyze the convergence properties of the molecular evolutionary algorithms on simulated DNA computers. The performance of the algorithms is demonstrated on the task of simulating the cognitive process of learning a language model from a drama corpus to identify the style of an unknown drama. We also discuss other applications of the molecular evolutionary algorithms. In addition to their feasibility in DNA computing, which opens a new horizon of in vitro evolutionary computing, the molecular evolutionary algorithm provides unique properties that are distinguished from conventional evolutionary algorithms and makes a new addition to the arsenal of tools in evolutionary computation.
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Citations
Enzymatic Weight Update Algorithm for DNA-Based Molecular Learning.
TL;DR: An in vitro molecular algorithm is introduced that ‘learns’ molecular models from training data, opening the possibility of ‘machine learning’ in wet molecular systems, and an intuitive method of DNA data construction is introduced to dramatically reduce the number of unique DNA sequences needed to cover the large search space of feature sets.
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In Vitro Molecular Machine Learning Algorithm via Symmetric Internal Loops of DNA
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TL;DR: A novel molecular learning in vitro model in which symmetric internal loops of double-stranded DNA are exploited to measure the differences between training instances, thus enabling the molecules to learn from small errors.
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MLHN: A Hypernetwork Model for Multi-Label Classification
TL;DR: The results illustrate that the proposed MLHN achieves competitive performances against state-of-the-art multi-label classification algorithms in terms of both effectiveness and scalability with respect to the number of labels.
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Massively-Parallel Pattern Recognition through Evolutionary Molecular Hypernetwork in DNA Computing
Christina Baek,Je-Hwan Ryu,Jihoon Lee,Byoung-Tak Zhang +3 more
- 01 Jun 2015
TL;DR: This paper demonstrates the making of a random single-stranded DNA library through the use of ligation and isolation techniques, and validates the experimental steps required to implement the evolutionary molecular Hypernetwork in DNA Computing.
1
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