Version Space Learning with DNA Molecules

Lim, Hee-Woong; Yun, Jieun; Jang, Hae-Man; Chai, Young-Gyu; Yoo, Seunghwa; Zhang, Byoung-Tak

doi:10.1007/3-540-36440-4_13

Cited by 7 publications

(8 citation statements)

References 6 publications

(7 reference statements)

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“…[41], [48]. The evolutionary operators of matching, selection, and amplification are performed by DNA chemistry based on molecular recognition.…”

Section: Box 3: Learning Hypernetworkmentioning

confidence: 99%

Hypernetworks: A Molecular Evolutionary Architecture for Cognitive Learning and Memory

Zhang

2008

IEEE Comput. Intell. Mag.

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Recent interest in human-level intelligence suggests a rethink of the role of machine learning in computational intelligence. We argue that without cognitive learning the goal of achieving human-level synthetic intelligence is far from completion. Here we review the principles underlying human learning and memory, and identify three of them, i.e., continuity, glocality, and compositionality, as the most fundamental to human-level machine learning. We then propose the recently-developed hypernetwork model as a candidate architecture for cognitive learning and memory. Hypernetworks are a random hypergraph structure higher-order probabilistic relations of data by an evolutionary self-organizing process based on molecular selfassembly. The chemically-based massive interaction for information organization and processing in the molecular hypernetworks, referred to as hyperinteractionism, is contrasted with the symbolist, connectionist, and dynamicist approaches to mind and intelligence. We demonstrate the generative learning capability of the hypernetworks to simulate linguistic recall memory, visual imagery, and language-vision crossmodal translation based on a video corpus of movies and dramas in a multimodal memory game environment. We also offer prospects for the hyperinteractionistic molecular mind approach to a unified theory of cognitive learning.

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“…[41], [48]. The evolutionary operators of matching, selection, and amplification are performed by DNA chemistry based on molecular recognition.…”

Section: Box 3: Learning Hypernetworkmentioning

confidence: 99%

Hypernetworks: A Molecular Evolutionary Architecture for Cognitive Learning and Memory

Zhang

2008

IEEE Comput. Intell. Mag.

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“…The method we present here is in some sense an extension of the memorybased learning (MBL) approach [8]. In MBL, the training examples are stored one copy for each instance.…”

Section: Pattern Classification and Dna Moleculesmentioning

confidence: 99%

A Bayesian Algorithm for In Vitro Molecular Evolution of Pattern Classifiers

Zhang

Jang

2005

DNA Computing

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Abstract. We use molecular computation to solve pattern classification problems. DNA molecules encode data items and the DNA library represents the empirical probability distribution of data. Molecular bio-lab operations are used to compute conditional probabilities that decide the class label. This probabilistic computational model distinguishes itself from the conventional DNA computing models in that the entire molecular population constitutes the solution to the problem as an ensemble. One important issue in this approach is how to automatically learn the probability distribution of the DNA-based classifier from observed data. Here we develop a molecular evolutionary algorithm inspired by directed evolution, and derive its molecular learning rule from Bayesian decision theory. We investigate through simulation the convergence behaviors of the molecular Bayesian evolutionary algorithm on a concrete problem from statistical pattern classification.

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“…A detailed description on the original version space learning in silico can be found in [10], and the DNA based method is described in [8]. In this section, we explain how a network of the bead separation modules can organize the DNA based concept learning.…”

Section: Concept Learning On Locmentioning

confidence: 99%

“…It was also used to check whether a path contains a specific city in HPP or TSP problems [1]. Moreover, Boolean value verification in the SAT problem [3] and attribute-value checking for hypothesis space refinement in the DNA-based concept learning [8] are accomplished by this technique.…”

Section: Introductionmentioning

confidence: 99%

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A Lab-on-a-Chip Module for Bead Separation in DNA-Based Concept Learning

Lim

Jang²,

Ha³

et al. 2004

DNA Computing

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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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Version Space Learning with DNA Molecules

Cited by 7 publications

References 6 publications

Hypernetworks: A Molecular Evolutionary Architecture for Cognitive Learning and Memory

Hypernetworks: A Molecular Evolutionary Architecture for Cognitive Learning and Memory

A Bayesian Algorithm for In Vitro Molecular Evolution of Pattern Classifiers

A Lab-on-a-Chip Module for Bead Separation in DNA-Based Concept Learning

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