• Asian-Australasian Journal of Animal Sciences
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• v.29 no.7
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• pp.1044-1051
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• 2016

The present study compared the differential functions of two groups of adjuvants, Montanide incomplete Seppic adjuvant (ISA) series and Quil A, cholesterol, dimethyl dioctadecyl ammonium bromide, and Carbopol (QCDC) formulations, in chicken by analyzing published microarray data associated with each type of vaccine adjuvants. In the biological function analysis for differentially expressed genes altered by two different adjuvant groups, ISA series and QCDC formulations showed differential effects when chickens were immunized with a recombinant immunogenic protein of Eimeria. Among the biological functions, six categories were modified in both adjuvant types. However, with respect to "Response to stimulus", no biological process was modified by the two adjuvant groups at the same time. The QCDC adjuvants showed effects on the biological processes (BPs) including the innate immune response and the immune response to the external stimulus such as toxin and bacterium, while the ISA adjuvants modified the BPs to regulate cell movement and the response to stress. In pathway analysis, ISA adjuvants altered the genes involved in the functions related with cell junctions and the elimination of exogenous and endogenous macromolecules. The analysis in the present study could contribute to the development of precise adjuvants based on molecular signatures related with their immunological functions.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.1
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• pp.1-7
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• 2005

Cluster analysis of microarray data has been often used to find biologically relevant Broups of genes based on their expression levels. Since many functionally related genes tend to be co-expressed, by identifying groups of genes with similar expression profiles, the functionalities of unknown genes can be inferred from those of known genes in the same group. In this Paper we address a novel clustering approach, called seed clustering, and investigate its applicability for microarray data analysis. In the seed clustering method, seed genes are first extracted by computational analysis of their expression profiles and then clusters are generated by taking the seed genes as prototype vectors for target clusters. Since it has strong mathematical foundations, the seed clustering method produces the stable and consistent results in a systematic way. Also, our empirical results indicate that the automatically extracted seed genes are well representative of potential clusters hidden in the data, and that its performance is favorable compared to current approaches.

• Korean Journal of Biological Psychiatry
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• v.8 no.2
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• pp.203-207
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• 2001

The genome sequencing project has generated and will continue to generate enormous amounts of sequence data including 5 eukaryotic and about 60 prokaryotic genomes. Given this ever-increasing amounts of sequence information, new strategies are necessary to efficiently pursue the next phase of the genome project-the elucidation of gene expression patterns and gene product function on a whole genome scale. In order to assign functional information to the genome sequence, DNA chip(or gene microarray) technology was developed to efficiently identify the differential expression pattern of independent biological samples. DNA chip provides a new tool for genome expression analysis that may revolutionize many aspects of biotechnology including new drug discovery and disease diagnostics.

• 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.

• Journal of KIISE:Computer Systems and Theory
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• v.29 no.7
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• pp.411-421
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• 2002

In this Paper we propose a new Image analysis algorithm for microarray processing and a method to locate the position of the grid cell using the topology of the grid spots. Microarray is a device which enables a parallel experiment of 10 to 100 thousands of test genes in order to measure the gene expression. Because of the huge data obtained by a experiment automated image analysis is needed. The final output of this microarray experiment is a set of 16-bit gray level image files which consist of grid-structured spots. In this paper we propose one algorithm which located the address of spots (spot indices) using graph structure from image data and a method which determines the precise location and shape of each spot by measuring the inclination of grid structure. Several experiments are given from real data sets.

• The Korean Journal of Applied Statistics
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• v.21 no.3
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• pp.523-535
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• 2008

Initial work of microarray data analysis focused on identification of differentially expressed genes, and recently, the focus has moved to discovering significant sets of functionally related genes. We describe some problems of GSEA and PAGE, and propose a modified method to identify significant gene sets. The results based on a simulated experiment and real data analysis using a set of publicly available data show the superiority of the newly proposed method, GSA-AT, in detecting significant pathways with the accurate prediction.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.4
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• pp.545-549
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• 2009

Microarray data sets could contain thousands of gene expression levels and have been considered as an important source from which meaningful patterns could be extracted for further analysis in biological studies. It is sometimes necessary to retrieve out specific genes or samples of analyst's interest in an effective way. This paper is concerned with a method to make use of fuzzy signature set in order to filter out genes or samples which satisfy complicated constraints as well as simple ones. Fuzzy signatures are an extension of vector valued fuzzy sets, in which elements of the vector are allowed to have a vector. Fuzzy signature sets are similar to fuzzy signatures except that their leaf elements are fuzzy sets defined on the interval [0,1]. This paper introduces an extension of fuzzy signature sets which specifies aggregation operators at each internal node and comparison operators for aggregation. It also shows how to use the extended fuzzy signature sets in microarray data retrieval and some examples of its usage.

• Proceedings of the Korea Society of Environmental Toocicology Conference
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• 2003.10a
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• pp.173-173
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• 2003

With the introduction of DNA microarrays, a high throughput analysis of gene expression is now possible as a replacement to the traditional time-consuming Southern-blot analysis. This cDNA microarray should be ahighly favored technology in the area of molecular toxicology or analysis of environmental stresses.In this study, therefore, we developed a novel cDNA microarray for analyzing stress-specific responses in japanese Medaka fish. In the design and fabrication of this stress specific functional cDNA microarray, 123 different genes in Medaka fish were selected from eighteen different stress responsive groups and spotted on a 25${\times}$75 mm glass surface. After exposure of the fish to bisphenol A which is the one of the well-known endocrine disrupting chemicals (EDCs), over 1 or 10 days, the responses of the DNA chip were found to show distinct expression patterns according to the mode of toxic actions from environmental toxicants. As a results, they showed specific gene expression pattern to bisphenol A, additionally, the chemical spesific biomarkers could be suggested based on the chip analysis data. Therefore, this chip can be used to monitor stress responses of unknown and/or known toxic chemicals using Medaka fish and may be used for the further development of biomarkers by utilizing the gene expression patterns for known contaminants.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2000.11a
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• pp.74-82
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• 2000

Prostate cancer initially responds and regresses in response to androgen depletion therapy, but most human prostate cancers will eventually recur, and re-grow as an androgen independent tumor. Once these tumors become hormone refractory, they usually are incurable leading to death for the patient. Little is known about the molecular details of how prostate cancer cells regress following androgen ablation and which genes are involved in the androgen independent growth following the development of resistance to therapy. Such knowledge would reveal putative drug targets useful in the rational therapeutic design to prevent therapy resistance and control androgen independent growth. The application of genome scale technologies have permitted new insights into the molecular mechanisms associated with these processes. Specifically, we have applied functional genomics using high density cDNA microarray analysis for parallel gene expression analysis of prostate cancer in an experimental xenograft system during androgen withdrawal therapy, and following therapy resistance, The large amount of expression data generated posed a formidable bioinformatics challenge. A novel template based gene clustering algorithm was developed and applied to the data to discover the genes that respond to androgen ablation. The data show restoration of expression of androgen dependent genes in the recurrent tumors and other signaling genes. Together, the discovered genes appear to be involved in prostate cancer cell growth and therapy resistance in this system. We have also developed and applied tissue microarray (TMA) technology for high throughput molecular analysis of hundreds to thousands of clinical specimens simultaneously. TMA analysis was used for rapid clinical translation of candidate genes discovered by cDNA microarray analysis to determine their clinical utility as diagnostic, prognostic, and therapeutic targets. Finally, we have developed a bioinformatic approach to combine pharmacogenomic data on the efficacy and specificity of various drugs to target the discovered prostate cancer growth associated candidate genes in an attempt to improve current therapeutics.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2006.02a
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• pp.20-28
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• 2006

최근의 microarray 기술의 발달로 인해 점점 더 많은 양의 mRNA 발현 데이터가 쌓여 가고 있다. 이제는 데이터를 만드는 단계보다는 데이터로부터 중요한 생물학적 의미를 끌어내는 것이 더욱 중요한 일이 되었다. micorarray 기술이 처음 도입된 이후로, 많은 앨고리즘과 소프트웨어가 개발되어, 실험자들이 microarray 데이터로부터 생물학적 의미를 끌어내는 작업을 도와주어 왔다. 그런데, 이전의 데이터 마이닝 방법들은 거의 예외 없이 전체 데이터로부터 선택된 몇 십, 몇 백 개의 유전자 리스트로부터 출발한다. 그런데, 이러한 방법 (over-representation analysis, ORA로 줄임)은 몇 가지 한계를 가지고 있어서, 최근에는 전체 데이터로부터 의미 있는 유전자 세트 (gene set)를 찾아내는 방법들이 도입되었다. 본 세미나는 이런 방법들, 줄여서 gene set analysis라 함, 에 사용되는 앨고리즘들과 소프트웨어들을 비교, 검토하고자 한다.