• KIPS Transactions on Computer and Communication Systems
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• v.9 no.11
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• pp.247-249
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• 2020

In the era of the 4th industrial revolution, the demand for convergence between ICT technologies is increasing in various fields. Accordingly, a new term that combines data, network, and artificial intelligence technology, DNA (Data, Network, AI) is in use. and has recently become a hot topic. DNA has various potential technology to be able to develop intelligent application in the real world. Therefore, this paper introduces the reviewed papers on the service image placement mechanism based on the logical fog network, the mobility support scheme based on machine learning for Industrial wireless sensor network, the prediction of the following BCI performance by means of spectral EEG characteristics, the warning classification method based on artificial neural network using topics of source code and natural language processing model for data visualization interaction with chatbot, related on DNA technology.

• KSBB Journal
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• v.16 no.4
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• pp.381-388
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• 2001

A program for integrated gene expression profile analysis such as hierarchical clustering, K-means, fuzzy c-means, self-organizing map(SOM), principal component analysis(PCA), and singular value decomposition(SVD) was made for DNA chip data anlysis by using Matlab. It also contained the normalization method of gene expression input data. The integrated data anlysis program could be effectively used in DNA chip data analysis and help researchers to get more comprehensive analysis view on gene expression data of their own.

• Journal of the Korean Data and Information Science Society
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• v.17 no.4
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• pp.1161-1167
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• 2006

Since 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. The existing analysis tools for DNA microarray data have many difficult problems by complicated instructions, and dependency on data types and operating system, and high cost, etc. In this paper, we design and implement the web-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.

• Korean Journal of Plant Taxonomy
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• v.48 no.3
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• pp.206-217
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• 2018

The nucleotide sequences of the chloroplast rbcL, matK, and psbA-trnH and nuclear internal transcribed spacer (ITS) regions were determined from all species of Viburnum in Korea with multiple accessions to reconstruct the phylogeny and to evaluate the utility of the DNA sequences as DNA barcodes. The results of phylogenetic analyses of the cpDNA and ITS data are consistent with the findings of previous studies of Viburnum. Four morphologically closely related species, V. dilatatum, V. erosum, V. japonicum, and V. wrightii, were included in a strongly supported sister clade of V. koreanum and V. opulus. Viburnum odoratissimum is suggested to be sister to the V. dilatatum/V. koreanum clade in the cpDNA data, while V. odoratissimum is a sister to V. furcatum in the ITS data. Viburnum burejaeticum and V. carlesii are strongly supported as monophyletic. Our analyses of DNA barcode regions from multiple accessions of the species of Viburnum in Korea confirm that six out of ten species in Korea can be discriminated at the species level. The V. dilatatum complex can be separated from the remaining species according to molecular data, but the resolution power to differentiate a species within the complex is weak. This study suggests that regional DNA barcodes are useful for molecular species identification in the case of Viburnum when flowering or fruiting materials are not available.

• Journal of Life Science
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• v.31 no.4
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• pp.410-417
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• 2021

Next-generation sequencing (NGS) is a high-throughput technique for sequencing large numbers of DNA fragments that are prepared from a genome. This sequencing technique has been used to elucidate whole genome sequences of living organisms and to analyze complementary DNA (cDNA) or chromatin immunoprecipitated DNA (ChIPed DNA) at the genome level. After NGS, the use of proper tools is important for processing and analyzing data with reasonable parameters. However, handling large-scale sequencing data and programing for data analysis can be difficult. The Galaxy platform, a public web service system, provides many different tools for NGS data analysis, and it allows researchers to analyze their data on a web browser with no deep knowledge about bioinformatics and/or programing. In this study, we explain the procedure for preparing chromatin immunoprecipitation-sequencing (ChIP-seq) libraries and steps for analyzing ChIP-seq data using the Galaxy platform. The data analysis steps include the NGS data upload to Galaxy, quality check of the NGS data, premapping processes, read mapping, the post-mapping process, peak-calling and visualization by window view, heatmaps, average profile, and correlation analysis. Analysis of our histone H3K4me1 ChIP-seq data in K562 cells shows that it correlates with public data. Thus, NGS data analysis using the Galaxy platform can provide an easy approach to bioinformatics.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.8
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• pp.319-326
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• 2007

To perform fast searching in massive data such as DNA strings, the most efficient method is to construct full-text index data structures of given strings. The widely used full-text index structures are suffix trees and suffix arrays. Since the suffix may uses less space than the suffix tree, the suffix array is proper for DNA strings. Previously developed construction algorithms of suffix arrays are not suitable for DNA strings since those are designed for integer alphabets. We propose a fast algorithm to construct suffix arrays on DNA strings whose alphabet sizes are fixed by 4. We reduce the construction time by improving encoding and merging steps on Kim et al.[1]'s algorithm. Experimental results show that our algorithm constructs suffix arrays on DNA strings 1.3-1.6 times faster than Kim et al.'s algorithm, and also for other algorithms in most cases.

• KSBB Journal
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• v.16 no.6
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• pp.538-546
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• 2001

An integrated data analysis program for DNA chip containing normalization, FDM analysis, various kinds of clustering methods, PCA, and SVD was applied to analyze combined yeast cell cycle data. This paper includes both comparisons of some clustering algorithms such as K-means, SOM and furry c-means and their results. For further analysis, clustering results from the integrated analysis program was used for function assignments to each cluster and for motif analysis. These results show an integrated analysis view on DNA chip data.

• Journal of Internet Computing and Services
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• v.19 no.2
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• pp.1-7
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• 2018

Due to advances in DNA sequencing technologies, its medical value continues to grow. However, once genome data leaked, it cannot be revoked, and disclosure of personal genome information impacts a large group of individuals. Therefore, secure techniques for managing genomic big data should be developed. We first propose a privacy-preserving inner product protocol for large data sets using the homomorphic encryption of Gentry et al., and then we introduce an efficient privacy-preserving DNA matching protocol based on the proposed protocol. Our efficient protocol satisfies the requirements of correctness, confidentiality, and privacy.

• Journal of KIISE:Computer Systems and Theory
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• v.30 no.10
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• pp.544-555
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• 2003

Since the result data from DNA microarray experiments contain a lot of gene expression information, adequate analysis methods are required. Hierarchical clustering is widely used for analysis of gene expression profiles. In this paper, we study leaf-ordering, which is a post-processing for the dendrograms output by hierarchical clusterings to improve the efficiency of DNA microarray data analysis. At first, we analyze existing leaf-ordering algorithms and then present new approaches for leaf-ordering. And we introduce a software HCLO(Hierarchical Clustering ＆ Leaf-Ordering Tool) that is our implementation of hierarchical clustering, some of existing leaf-ordering algorithms and those presented in this paper.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2000.11a
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• pp.59-60
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• 2000

The past few years have seen a dramatic increase in gene expression data on the basis of DNA microarrays or DNA chips. Going beyond a generic view on the genome, microarray data are able to distinguish between gene populations in different tissues of the same organism and in different states of cells belonging to the same tissue. This affords a cell-wide view of the metabolic and regulatory processes under different conditions, building an effective basis for new diagnoses and therapies of diseases. In this talk we present machine learning techniques for effective mining of DNA microarray data. A brief introduction to the research field of machine learning from the computer science and artificial intelligence point of view is followed by a review of recently-developed learning algorithms applied to the analysis of DNA chip gene expression data. Emphasis is put on graphical models, such as Bayesian networks, latent variable models, and generative topographic mapping. Finally, we report on our own results of applying these learning methods to two important problems: the identification of cell cycle-regulated genes and the discovery of cancer classes by gene expression monitoring. The data sets are provided by the competition CAMDA-2000, the Critical Assessment of Techniques for Microarray Data Mining.