• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.10
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• pp.118-127
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• 2014

DNA data storage refers to any technique for storing massive digital data in base sequence of DNA and has been recognized as the future storage medium recently. This paper presents an information hiding method for DNA data storage that the massive data is hidden in non-coding strand based on DNA steganography. Our method maps the encrypted data to the data base sequence using the numerical mapping table and then hides it in the non-coding strand using the key that consists of the seed and sector length. Therefore, our method can preserve the protein, extract the hidden data without the knowledge of host DNA sequence, and detect the position of mutation error. Experimental results verify that our method has more high data capacity than conventional methods and also detects the positions of mutation errors by the parity bases.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.838-841
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• 2005

DNA Chip is able to show DNA-Data that includes diseases of sample to User by using complementary characters of DNA. So this paper studied Neural Network algorithm for Image data processing of DNA-chip. DNA chip outputs image data of colors and intensities of lights when some sample DNA is putted on DNA-chip, and we can classify pattern of these image data on user pc environment through artificial neural network and some of image processing algorithms. Ultimate aim is developing of pattern classifying algorithm, simulating this algorithm and so getting information of one's diseases through applying this algorithm. Namely, this paper study artificial neural network algorithm for classifying pattern of image data that is obtained from DNA-chip. And, by using histogram, gradient edge, ANN and learning algorithm, we can analyze and classifying pattern of this DNA-chip image data. so we are able to monitor, and simulating this algorithm.

• Genomics & Informatics
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• v.9 no.2
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• pp.89-91
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• 2011

DNA chips are becoming increasingly popular as a convenient way to perform vast amounts of experiments related to genes on a single chip. And the importance of analyzing the data that is provided by such DNA chips is becoming significant. A very important analysis on DNA chip data would be clustering genes to identify gene groups which have similar properties such as cancer. Clustering data for DNA chips usually deal with a large search space and has a very fuzzy characteristic. The Particle Swarm Optimization algorithm which was recently proposed is a very good candidate to solve such problems. In this paper, we propose a clustering mechanism that is based on the Particle Swarm Optimization algorithm. Our experiments show that the PSO-based clustering algorithm developed is efficient in terms of execution time for clustering DNA chip data, and thus be used to extract valuable information such as cancer related genes from DNA chip data with high cluster accuracy and in a timely manner.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.7
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• pp.1424-1434
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• 2009

DNA computing-inspired pattern classification based on the hypernetwork model is a novel approach to pattern classification problems. The hypernetwork model has been shown to be a powerful tool for multi-class data analysis. However, the ordinary hypernetwork model has limitations, such as operating sequentially only. In this paper, we propose a efficient implementing method of DNA computing-inspired pattern classifier using GPU. We show simulation results of multi-class pattern classification from hand-written digit data, DNA microarray data and 8 category scene data for performance evaluation. and we also compare of operation time of the proposed DNA computing-inspired pattern classifier on each operating environments such as CPU and GPU. Experiment results show competitive diagnosis results over other conventional machine learning algorithms. We could confirm the proposed DNA computing-inspired pattern classifier, designed on GPU using CUDA platform, which is suitable for multi-class data classification. And its operating speed is fast enough to comply point-of-care diagnostic purpose and real-time scene categorization and hand-written digit data classification.

• Journal of the Korean Data and Information Science Society
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• v.17 no.1
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• pp.53-62
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• 2006

DNA microarray data analysis is a new technology to investigate the expression levels of thousands of genes simultaneously. Since DNA microarray data structures are various and complicative, the data are generally stored in databases for approaching to and controlling the data effectively. But we have some difficulties to analyze and control the data when the data are stored in the several database management systems or that the data are stored to the file format. The existing analysis tools for DNA microarray data have many difficult problems by complicated instructions, and dependency on data types and operating system. In this paper, we design and implement network-based analysis tool for obtaining to useful information from DNA microarray data. When we use this tool, we can analyze effectively DNA microarray data without special knowledge and education for data types and analytical methods.

• Genomics & Informatics
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• v.9 no.3
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• pp.134-135
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• 2011

DNA chips are used for experiments on genes and provide useful information that could be further analyzed. Using the data extracted from the DNA chips to find useful patterns or information has become a very important issue. In this paper, we explain the application developed for classifying DNA chip data using a classification method based on the Particle Swarm Optimization (PSO) algorithm. Considering that DNA chip data is extremely large and has a fuzzy characteristic, an algorithm that imitates the ecosystem such as the PSO algorithm is suitable to be used for analyzing such data. The application enables researchers to customize the PSO algorithm parameters and see detail results of the classification rules.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.196-202
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• 2021

DNA sequencing provides fundamental data in genomics, bioinformatics, biology and many other research areas. With the emergent evolution in DNA sequencing technology, a massive amount of genomic data is produced every day, mainly DNA sequences, craving for more storage and bandwidth. Unfortunately, managing, analyzing and specifically storing these large amounts of data become a major scientific challenge for bioinformatics. Those large volumes of data also require a fast transmission, effective storage, superior functionality and provision of quick access to any record. Data storage costs have a considerable proportion of total cost in the formation and analysis of DNA sequences. In particular, there is a need of highly control of disk storage capacity of DNA sequences but the standard compression techniques unsuccessful to compress these sequences. Several specialized techniques were introduced for this purpose. Therefore, to overcome all these above challenges, lossless compression techniques have become necessary. In this paper, it is described a new DNA compression mechanism of pattern matching extended Compression algorithm that read the input sequence as segments and find the matching pattern and store it in a permanent or temporary table based on number of bases. The remaining unmatched sequence is been converted into the binary form and then it is been grouped into binary bits i.e. of seven bits and gain these bits are been converted into an ASCII form. Finally, the proposed algorithm dynamically calculates the compression ratio. Thus the results show that pattern matching extended Compression algorithm outperforms cutting-edge compressors and proves its efficiency in terms of compression ratio regardless of the file size of the data.

• The KIPS Transactions:PartD
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• v.17D no.3
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• pp.175-184
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• 2010

Experiments and research on genes have become very convenient by using DNA chips, which provide large amounts of data from various experiments. The data provided by the DNA chips could be represented as a two dimensional matrix, in which one axis represents genes and the other represents samples. By performing an efficient and good quality clustering on such data, the classification work which follows could be more efficient and accurate. In this paper, we use a bio-inspired algorithm called the Particle Swarm Optimization algorithm to propose an efficient clustering mechanism for large amounts of DNA chip data, and show through experimental results that the clustering technique using the PSO algorithm provides a faster yet good quality result compared with other existing clustering solutions.

• Korean Journal of Plant Taxonomy
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• v.50 no.4
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• pp.403-412
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• 2020

Colonies of three independent gametophytes (one that is filamentous and two that are ribbon-like) without sporophytes occur in Gyeonggi-do, Gangwon-do, Gyeongsang-do, and Jeju-do, Korea. They have a moss-like appearance at first sight, with tiny plantlets and gemmae, and grow in cool, shaded, relatively deep dint places of large rocks, such as the small caves in high mountains, close to valleys. The gametophytes were identified based on morphological and molecular data by chloroplast DNA (cpDNA) sequence data (rbcL, rps4 gene and rps4-trnS intergenic spacer). Here, rbcL, rps4 gene and rps4-trnS intergenic spacer data of one independent gametophyte distributed in Korea have the same morphology, DNA sequence and monophyletic group as Crepidomanes intricatum from the eastern United States. They also share the same cpDNA data with Crepidomanes schmidtianum recently reported from Korea. The other independent gametophyte should be Hymenophyllum wrightii based on cpDNA data. The last one was presumed to be Pleurosoriopsis makinoi based on molecular data. The taxonomic status was confirmed to be the forma of Hymenophyllum wrightii through a revision of Hymenophyllum wrightii f. serratum based on molecular data.

• Journal of the Korean Chemical Society
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• v.28 no.3
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• pp.185-193
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• 1984

At the spermine concentration to cover the number of the binding site of spermine 0.016 per nucleotide, the Hill coefficient of the ethidium binding to the calf thymus DNA was 1.7, while the value was 0.38 in the absence of the spermine. On the basis of the data, together with other present data on the viscometric titration of the DNA with spermine and anomalous absorbance-temperature profile at 260nm and viscosity-temperature profile, it can be speculated that allosteric propagation of the conformational transition induced by the binding of the spermine may be involved in the monomolecular collapse of the DNA to a condensed structure.