• The Korean Journal of Applied Statistics
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• v.17 no.3
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• pp.419-430
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• 2004

Since cDNA microarray experiments can monitor expression levels for thousands of genes simultaneously, the experimental designs and their analyzing methods are very important for successful analysis of microarray data. The loop design is discussed for selecting differentially expressed genes among several treatments and the analysis of variance method is introduced to normalize microarray data and provide estimates of the interesting quantities. MA-ANOVA is used to illustrate this method on a recently collected loop designed microarray data at Cancer Metastasis Research Center, Yonsei University.

• The Journal of the Korea Contents Association
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• v.9 no.8
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• pp.18-31
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• 2009

Improved reliability of microarray data and its reproducibility lead to recent increment in demand of data sharing and utilization among laboratories, but house-keeping and publicly opened microarray experimental data can hardly be accessed and utilized since they are in heterogeneous formats according to the various experimental methods and microarray platforms. In this paper, we propose a microarray sharing method which can easily retrieve and integrate microarray data from different experiment platforms, data formats, normalization methods, and analysis methods. Our system is based on web-service technology. The biologists of each site are able to search UDDI(Universal Description, Discovery, and Integration) registry, and download microarray data with common data structure of standard format recommended by MGED(Microarray Gene Expression Databases) society. The common data structure defined in this paper consists of IDF(Investigation Design Format), ADF(Array Design Format), SDRF(Sample and Relationship Format), and EDF(Expression Data Format). These components play role as templates to integrate microarray data with various structure and can be stored in standard formats such as MAGE-ML, MAGE-TAB, and XML Schema. In addition, our system provides advanced tools of automatic microarray data submitter and file manager to manipulate local microarray data efficiently.

• 한국데이터정보과학회:학술대회논문집
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• 2006.04a
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• pp.121-129
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• 2006

Two color or cDNA microarrays are extensively used to study relative expression levels of thousands of genes simultaneously. 0かy two tissue samples can be hybridized on a single microarray slide. Thus, a microarray slide necessarily forms an incomplete block design with block size two when more than two tissue samples are under study. We also need to control for variability in gene expression values due to the two dyes. Thus, red and green dyes form the second blocking factor in addition to slides. General design problem for these microarray experiments is discussed in this paper. Designs for factorial cDNA microarrays are also discussed.

• 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.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2004.11a
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• pp.272-278
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• 2004

Recently, Pepe et al. (2003) employed the receiver operating characteristic (ROC) approach to rank candidate genes from a microarray experiment that can be used for the biomarker development with the ultimate purpose of the population screening of a cancer, In the cancer microarray experiment based on n patients the researcher often wants to compare the tumor tissue with the normal tissue within the same individual using a common reference RNA. This design is referred to as a reference design or an indirect design. Ideally, this experiment produces n pairs of microarray data, where each pair consists of two sets of microarray data resulting from reference versus normal tissue and reference versus tumor tissue hybridizations. However, for certain individuals either normal tissue or tumor tissue is not large enough for the experimenter to extract enough RNA for conducting the microarray experiment, hence there are missing values either in the normal or tumor tissue data. Practically, we have $n_1$ pairs of complete observations, $n_2$ 'normal only' and $n_3$ 'tumor only' data for the microarray experiment with n patients, where n=$n_1$+$n_2$+$n_3$. We refer to this data set as a mixed data set, as it contains a mix of fully observed and partially observed pair data. This mixed data set was actually observed in the microarray experiment based on human tissues, where human tissues were obtained during the surgical operations of cancer patients. Pepe et al. (2003) provide the rationale of using ROC approach based on two independent samples for ranking candidate gene instead of using t or Mann -Whitney statistics. We first modify ROC approach of ranking genes to a paired data set and further extend it to a mixed data set by taking a weighted average of two ROC values obtained by the paired data set and two independent data sets.

• 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.

• Journal of KIISE:Software and Applications
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• v.35 no.8
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• pp.501-511
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• 2008

Probe design is one of the essential tasks in successful DNA microarray experiments. The requirements for probes vary as the purpose or type of microarray experiments. In general, most previous works use the simple filtering approach with the fixed threshold value for each requirement. Here, we formulate the probe design as a multiobjective optimization problem with the two objectives and solve it using ${\epsilon}$-multiobjective evolutionary algorithm. The suggested approach was applied in designing probes for 19 types of Human Papillomavirus and 52 genes in Arabidopsis Calmodulin multigene family and successfully produced more target specific probes compared to well known probe design tools such as OligoArray and OligoWiz.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06a
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• pp.82-84
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• 2006

최근의 생물학적인 연구에 DNA microarray가 널리 쓰이고 있기 때문에, 이러한 DNA microarray를 구성하는데 필요한 probe design 작업의 중요성이 점차 커져가고 있다. 이 논문에서는 probe design 문제를 thermodynamic fitness function이 2개인 multi-objective optimization 작업으로 변환한 뒤, ${\epsilon}$-multiobjective evolutionary algorithm을 이용하여 probe set을 찾는다. 또한, probe 탐색공간의 크기를 줄이기 위하여 각 DNA sequence의 primer 영역을 찾는 작업을 진행하며, 사용자가 직접 프로그램을 테스트할 수 있는 웹사이트를 제공한다. 실험 대상으로는 mycoides를 선택하였으며, 이 논문에서 제안된 방법을 사용하여 성공적으로 probe set을 발견할 수 있었다.

• Communications for Statistical Applications and Methods
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• v.14 no.1
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• pp.205-213
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• 2007

In this paper, the normal mixture model subjected to general linear restriction for component-means based on linear regression is proposed, and its fitting method by EM algorithm and Lagrange multiplier is provided. This model is applied to gene clustering of microarray expression data, which demonstrates it has very good performances for real data set. This model also allows to obtain the clusters that an analyst wants to find out in the fashion that the hypothesis for component-means is represented by the design matrices and the linear restriction matrices.

• Microbiology and Biotechnology Letters
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• v.31 no.2
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• pp.182-190
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• 2003

As DNA microarrays are widely used recently, the amount of microarray data is exponentially increasing. Until now, however, no domestic system is available for the efficient management of such data. Because the number of experimental data in a specific laboratory is limited, it is necessary to avoid redundant experiments and to accumulate the results using a shared data management system for microarrays. In this paper, a system named WEMA (WEb management of Micro Arrays) was designed and implemented to manage and process the microarray data. WEMA system was designed to include the basic feature of MIAME (Minimal Information About a Microarray Experiment), and general data units were also defined in the system in order to systematically manage the data. The WEMA system has three main features: efficient management of microarray data, integration of input/ouput data, and metafile processing. The system was tested with actual microarray data produced by a molecular biology laboratory, and we found that the biologists could systematically manage and easily analyze the microarray data. As a consequence, the researchers could reduce the cost of data exchange and communication.