Abstract. Version space is used in inductive concept learning to represent the hypothesis space where the goal concept is expressed as a conjunction of attribute values. The size of the version space increases exponentially with the number of attributes. We present an efficient method for representing the version space with DNA molecules and demonstrate its effectiveness by experimental results. Primitive operations to maintain a version space are derived and their DNA implementations are described. We also propose a novel method for robust decision-making that exploits the huge number of DNA molecules representing the version space.
Abstract. Affinity separation with magnetic beads is an important and widely used technique for DNA computing. We have designed and implemented an experimental lab-on-a-chip module for affinity-bead separation for DNA-based concept learning. Magnetic beads with DNA-probe sequences immobilized on their surface were used to select target strands, and these beads are restrained in the channel by a permanent magnet on top of the module. The separation process consists of two steps, i.e. hybridization and denaturation. We confirmed the separation process by a mixed solution that contains FITC modified strands, and measured the yield by UV spectrophotometer. The experimental results demonstrate a successful separation of the mixed DNA.
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