Enzymatic Weight Update Algorithm for DNA-Based Molecular Learning
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 syste...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-04-01
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| Series: | Molecules |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1420-3049/24/7/1409 |
| _version_ | 1818422596324032512 |
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| author | Christina Baek Sang-Woo Lee Beom-Jin Lee Dong-Hyun Kwak Byoung-Tak Zhang |
| author_facet | Christina Baek Sang-Woo Lee Beom-Jin Lee Dong-Hyun Kwak Byoung-Tak Zhang |
| author_sort | Christina Baek |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T13:28:46Z |
| format | Article |
| id | doaj.art-4f4dea5a370f46c582810fdaadf70490 |
| institution | Directory Open Access Journal |
| issn | 1420-3049 |
| language | English |
| last_indexed | 2024-12-14T13:28:46Z |
| publishDate | 2019-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj.art-4f4dea5a370f46c582810fdaadf704902022-12-21T22:59:45ZengMDPI AGMolecules1420-30492019-04-01247140910.3390/molecules24071409molecules24071409Enzymatic Weight Update Algorithm for DNA-Based Molecular LearningChristina Baek0Sang-Woo Lee1Beom-Jin Lee2Dong-Hyun Kwak3Byoung-Tak Zhang4Interdisciplinary Program in Neuroscience, Seoul National University, Seoul 08826, KoreaSchool of Computer Science and Engineering, Seoul National University, Seoul 08826, KoreaSchool of Computer Science and Engineering, Seoul National University, Seoul 08826, KoreaInterdisciplinary Program in Neuroscience, Seoul National University, Seoul 08826, KoreaInterdisciplinary Program in Neuroscience, Seoul National University, Seoul 08826, KoreaRecent 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.https://www.mdpi.com/1420-3049/24/7/1409molecular computingmolecular learningDNA computingself-organizing systemspattern classificationmachine learning |
| spellingShingle | Christina Baek Sang-Woo Lee Beom-Jin Lee Dong-Hyun Kwak Byoung-Tak Zhang Enzymatic Weight Update Algorithm for DNA-Based Molecular Learning Molecules molecular computing molecular learning DNA computing self-organizing systems pattern classification machine learning |
| title | Enzymatic Weight Update Algorithm for DNA-Based Molecular Learning |
| title_full | Enzymatic Weight Update Algorithm for DNA-Based Molecular Learning |
| title_fullStr | Enzymatic Weight Update Algorithm for DNA-Based Molecular Learning |
| title_full_unstemmed | Enzymatic Weight Update Algorithm for DNA-Based Molecular Learning |
| title_short | Enzymatic Weight Update Algorithm for DNA-Based Molecular Learning |
| title_sort | enzymatic weight update algorithm for dna based molecular learning |
| topic | molecular computing molecular learning DNA computing self-organizing systems pattern classification machine learning |
| url | https://www.mdpi.com/1420-3049/24/7/1409 |
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