Molecular Learning with DNA Kernel Machinesopen access
- Authors
- Noh, Yung-Kyun; Lee, Daniel D.; Yang, Kyung-Ae; Kim, Cheongtag; Zhang, Byoung-Tak
- Issue Date
- Jan-2015
- Publisher
- ELSEVIER SCI LTD
- Keywords
- DNA computing; Kernel methods; Learning in vitro; Machine learning; Molecular algorithms
- Citation
- BIOSYSTEMS, v.137, pp.73 - 83
- Indexed
- SCIE
SCOPUS
- Journal Title
- BIOSYSTEMS
- Volume
- 137
- Start Page
- 73
- End Page
- 83
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/158052
- DOI
- 10.1016/j.biosystems.2015.06.007
- ISSN
- 0303-2647
- Abstract
- We present a computational learning method for bio-molecular classification. This method shows how to design biochemical operations both for learning and pattern classification. As opposed to prior work, our molecular algorithm learns generic classes considering the realization in vitro via a sequence of molecular biological operations on sets of DNA examples. Specifically, hybridization between DNA molecules is interpreted as computing the inner product between embedded vectors in a corresponding vector space, and our algorithm performs learning of a binary classifier in this vector space. We analyze the thermodynamic behavior of these learning algorithms, and show simulations on artificial and real datasets as well as demonstrate preliminary wet experimental results using gel electrophoresis.
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Collections - 서울 공과대학 > 서울 컴퓨터소프트웨어학부 > 1. Journal Articles
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