• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.6
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• pp.538-542
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• 2001

DNA computing has been applied to the problem of getting an optimal soluting since Adleman's experiment. DNA computing uses strings with various length and four-type bases that makes more useful for finding a global optimal solutions of the complex multi-modal problems This paper presents DNA coding method finding optimal solution of the multi-modal function and compares the efficiency of this method with the genetic algorithms(GA). GA searches efffectively an optimal solution via the artificial evolution of individual group of binary string and DNA coding method uses DNA molecules and four-type bases denoted by the A(Ademine) C(Gytosine);G(Guanine)and T(Thymine). The selection, crossover, mutation operators are applied to both DNA coding algorithm and genetic algorithms and the comparison has been performed. The results show that the DNA based algorithm performs better than GA.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.456-459
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• 1999

In this paper, we propose a new neural network based on the DNA coding method. The initial population of the structure information and the weights for the neural network is generated, and then the descendants are chose with the Elitist selection by the genetic algorithm. The evolutionary technique and the suitable fitness measure are used to find a neural network with the fractal number of layers. which represents a good approximation to the given function.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.322-325
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• 2003

In this paper, we propose n new fuzzy model identification of time series system using wavelet transform and genetic DNA code. Generally, it is well known that the DNA coding method is more diverse in the knowledge expression and better in the optimization performance than the genetic algorithm (GA) because it can encode more plentiful genetic information based on the biological DNA. The proposed method can construct a fuzzy model using the wavelet transform, in which the coefficients are identified by the DNA coding method. Thus, we can effectively get the fuzzy model of the nonlinear system by using the advantages of both wavelet transform and DNA coding method. In order to demonstrate the superiority of the proposed method, it is compared with modeling method using the conventional GA.

• Animal cells and systems
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• v.2 no.1
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• pp.113-116
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• 1998

A cDNA clone coding for ribosomal protein 46 (rp46) which is a component of 60S ribosomal large subunit has been identified from Drosophila melanogaster. A cDNA clone encoding S. cerevisiae rp46 was used as a probe to screen a Drosophila larvae cDNA library. The DNA sequence analysis revealed that the cDNA coding for Drosophils rp46 contains a complete reading frame of 153 nucleotides coding for 51 amino acids. The deduced amino acid sequence showed 71-75% homology with those of other eukaryotic organisms. Northern blot analysis showed that about 1-kb rp46 transcripts are abundant throughout fly development. Whole mount embryonic mRNA in situ hybridization also showed no preferential distribution of the transcripts to any specific region. The chromosomal in situ hybridization revealed that the identified gene is localized at position 60C on the right arm of the second polytene chromosome with a possibility of single copy.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.224-227
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• 2000

In this Paper, we prepose a method of constructing neural networks using bio-inspired emergent and evolutionary concepts. This method is algorithm that is based on the characteristics of the biological DNA and growth of plants. Here is, we propose a constructing method to make a DNA coding method for production rule of L-system. L-system is based on so-called the parallel rewriting mechanism. The DNA coding method has no limitation in expressing the production rule of L-system. Evolutionary algorithms motivated by Darwinian natural selection are population based searching methods and the high performance of which is highly dependent on the representation of solution space. In order to verify the effectiveness of our scheme, we apply it to one step ahead prediction of Mackey-Glass time series, Sun spot data and KOSPI data.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2633-2635
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• 2000

In this paper, a new DNA coding method, namely modified DNA coding method based on the biological DNA and the evolution mechanism of genetic algorithm. In order to evaluate the propose algorithms, for an example, they are applied to the fuzzy control of parallel double inverted pendulum system. Simulation result show the method is effective in finding the fuzzy control rules and is more excellent than conventional methods in control the system.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.4
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• pp.315-323
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• 2000

In this paper, we propose a method of constructing neural networks using bio-inpired emergent and evolutionary concepts. This method is algorithm that is based on the characteristics of the biological DNA and growth of plants, Here is, we propose a constructing method to make a DNA coding method for production rule of L-system. L-system is based on so-called the parallel rewriting nechanism. The DNA coding method has no limitation in expressing the produlation the rule of L-system. Evolutionary algotithms motivated by Darwinaian natural selection are population based searching methods and the high performance of which is highly dependent on the representation of solution space. In order to verify the effectiveness of our scheme, we apply it one step ahead prediction of Mackey-Glass time series, Sunspot data and KOSPI data.

• Journal of Animal Science and Technology
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• v.60 no.10
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• pp.25.1-25.10
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• 2018

The central dogma of gene expression propounds that DNA is transcribed to mRNA and finally gets translated into protein. Only 2-3% of the genomic DNA is transcribed to protein-coding mRNA. Interestingly, only a further minuscule part of genomic DNA encodes for long non-coding RNAs (lncRNAs) which are characteristically more than 200 nucleotides long and can be transcribed from both protein-coding (e.g. H19 and TUG1) as well as non-coding DNA by RNA polymerase II. The lncRNAs do not have open reading frames (with some exceptions), 3`-untranslated regions (3'-UTRs) and necessarily these RNAs lack any translation-termination regions, however, these can be spliced, capped and polyadenylated as mRNA molecules. The flexibility of lncRNAs confers them specific 3D-conformations that eventually enable the lncRNAs to interact with proteins, DNA or other RNA molecules via base pairing or by forming networks. The lncRNAs play a major role in gene regulation, cell differentiation, cancer cell invasion and metastasis and chromatin remodeling. Deregulation of lncRNA is also responsible for numerous diseases in mammals. Various studies have revealed their significance as biomarkers for prognosis and diagnosis of cancer. The aim of this review is to overview the salient features, evolution, biogenesis and biological importance of these molecules in the mammalian system.

• Korean Journal of Microbiology
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• v.30 no.5
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• pp.397-402
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• 1992

The cDNA of RNA segment coding for VP7 of human rotavirus isolated from patient's stool at Seoul area was synthesized, amplified by polymerase chain reaction, field in with Klenow fragment of DNA polymerase I and cloned into pUC19. The cDNA sequence was determined and compared with that of VP7 coding RNA segments of group A rotaviruses isolates in foreign country. Over 90% sequence homology was found with serotyppe I sepcific WA1 and RE9 strains. Comparative analysis of the deduced amino acid sequences within the two variable regions (amino acid residue 87 through 101 and 208 through 221) with WA1 and RE9 strains also showed high degree of sequence similarity with each other.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.105-111
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• 2004

DNA computing has been using to solve TSP (Traveling Salesman Problems). However, when the typical DNA computing is applied to TSP, it can`t efficiently express vertices and weights of between vertices. In this paper, we proposed ACO (Algorithm for Code Optimization) that applies DNA coding method to DNA computing to efficiently express vertices and weights of between vertices for TSP. We applied ACO to TSP and as a result ACO could express DNA codes which have variable lengths and weights of between vertices more efficiently than Adleman`s DNA computing algorithm could. In addition, compared to Adleman`s DNA computing algorithm, ACO could reduce search time and biological error rate by 50% and could search for a shortest path in a short time.