title: Molecular learning with DNA kernel machines.
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.
authors.affiliation: Department of Mechanical and Aerospace Engineering, Seoul National University, Republic of Korea.
authors.forename: Yung-Kyun
authors.initials: YK
authors.lastname: Noh
authors.affiliation: Department of Electrical and Systems Engineering, University of Pennsylvania, USA.
authors.forename: Daniel D
authors.initials: DD
authors.lastname: Lee
authors.affiliation: Department of Medicine, Columbia University, USA.
authors.forename: Kyung-Ae
authors.initials: KA
authors.lastname: Yang
authors.affiliation: Department of Psychology, Seoul National University, Republic of Korea.
authors.forename: Cheongtag
authors.initials: C
authors.lastname: Kim
authors.affiliation: School of Computer Science and Engineering, Seoul National University, Republic of Korea. Electronic address: btzhang@bi.snu.ac.kr.
authors.forename: Byoung-Tak
authors.initials: BT
authors.lastname: Zhang
mesh_terms: D004247:DNA / Q000235:genetics*; D005822:Genetic Vectors; D009693:Nucleic Acid Hybridization; D013816:Thermodynamics
chemical_list: D004247:DNA
keywords: DNA computing; Kernel methods; Learning in vitro; Machine learning; Molecular algorithms