• Genomics & Informatics
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• v.9 no.1
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• pp.19-27
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• 2011

Gene Expression Omnibus (GEO) has kept the largest amount of gene-expression microarray data that have grown exponentially. Microarray data in GEO have been generated in many different formats and often lack standardized annotation and documentation. It is hard to know if preprocessing has been applied to a dataset or not and in what way. Standard-based integration of heterogeneous data formats and metadata is necessary for comprehensive data query, analysis and mining. We attempted to integrate the heterogeneous microarray data in GEO based on Minimum Information About a Microarray Experiment (MIAME) standard. We unified the data fields of GEO Data table and mapped the attributes of GEO metadata into MIAME elements. We also discriminated non-preprocessed raw datasets from others and processed ones by using a two-step classification method. Most of the procedures were developed as semi-automated algorithms with some degree of text mining techniques. We localized 2,967 Platforms, 4,867 Series and 103,590 Samples with covering 279 organisms, integrated them into a standard-based relational schema and developed a comprehensive query interface to extract. Our tool, GEOQuest is available at http://www.snubi.org/software/GEOQuest/.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.366-370
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• 2005

Tissue microarry is one of the high throughput technologies in the post-genomic era. Using tissue microarray, the researchers are able to investigate large amount of gene expressions at the level of DNA, RNA, and protein The important aspect of tissue microarry is its ability to assess a lot of biomarkers which have been used in clinical practice. To manipulate the categorical data of tissue microarray, we applied Bayesian network classifier algorithm. We identified that Bayesian network classifier algorithm could analyze tissue microarray data and integrating prior knowledge about gastric cancer could achieve better performance result. The results showed that relevant integration of prior knowledge promote the prediction accuracy of survival status of the immunohistochemical tissue microarray data of 18 tumor suppressor genes. In conclusion, the application of Bayesian network classifier seemed appropriate for the analysis of the tissue microarray data with clinical information.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.29-34
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• 2005

DNA microarray becomes a major tool for the investigation of global gene expression in all aspects of cancer and biomedical research. DNA microarray experiment generates enormous amounts of data and they are meaningful only in the context of a detailed description of microarrays, biomaterials, and conditions under which they were generated. MicroArray Gene Expression Data (MGED) society has established microarray standard for structured management of these diverse and large amount data. MGED MAGE-OM (MicroArray Gene Expression Object Model) is an object oriented data model, which attempts to define standard objects for gene expression. To assess the relevance of DNA microarray analysis of cancer research it is required to combine clinical and genomics data. MAGE-OM, however, does not have an appropriate structure to describe clinical information of cancer. For systematic integration of gene expression and clinical data, we create a new model, Cancer Genomics Object Model.

• 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.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2006.02a
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• pp.30-36
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• 2006

Tissue microarray (TMA) is an array-based technology allowing the examination of hundreds of tissue samples on a single slide. To handle, exchange, and disseminate TMA data, we need standard representations of the methods used, of the data generated, and of the clinical and histopathological information related to TMA data analysis. This study aims to create a comprehensive data model with flexibility that supports diverse experimental designs and with expressivity and extensibility that enables an adequate and comprehensive description of new clinical and histopathological data elements. We designed a Tissue Microarray Object Model (TMA-OM). Both the Array Information and the Experimental Procedure models are created by referring to Microarray Gene Expression Object Model, Minimum Information Specification For In Situ Hybridization and Immunohistochemistry Experiments (MISFISHIE), and the TMA Data Exchange Specifications (TMA DES). The Clinical and Histopathological Information model is created by using CAP Cancer Protocols and National Cancer Institute Common Data Elements (NCI CDEs). MGED Ontology, UMLS and the terms extracted from CAP Cancer Protocols and NCI CDEs are used to create a controlled vocabulary for unambiguous annotation. We implemented a web-based application for TMA-OM, supporting data export in XML format conforming to the TMA DES or the DTD derived from TMA-OM. TMA-OM provides a comprehensive data model for storage, analysis and exchange of TMA data and facilitates model-level integration of other biological models.

• Genomics & Informatics
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• v.5 no.2
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• pp.83-87
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• 2007

The analysis of DNA microarray data is a rapidly evolving area of bioinformatics, and various types of microarray are emerging as some of the most exciting technologies for use in biological and clinical research. In recent years, microarray technology has been utilized in various applications such as the profiling of mRNAs, assessment of DNA copy number, genotyping, and detection of methylated sequences. However, the analysis of these heterogeneous microarray platform experiments does not need to be performed separately. Rather, these platforms can be co-analyzed in combination, for cross-validation. There are a number of separate laboratory information management systems (LIMS) that individually address some of the needs for each platform. However, to our knowledge there are no unified LIMS systems capable of organizing all of the information regarding multi-platform microarray experiments, while additionally integrating this information with tools to perform the analysis. In order to address these requirements, we developed a web-based LIMS system that provides an integrated framework for storing and analyzing microarray information generated by the various platforms. This system enables an easy integration of modules that transform, analyze and/or visualize multi-platform microarray data.

• 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.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2003.10a
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• pp.147-154
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• 2003

Microarray information system is a complex system to manage, analyze and interpretate microarray gene expression data. Establishment of well-defined development process is very essential for understanding the complexity and organization of the system. We performed object-oriented analysis using Unified Modeling Language (UML) in specifying, visualizing and documenting microarray information system. The object-oriented analysis consists of three major steps: (i) use case modeling to describe various functionalities from the user's perspective (ii) dynamic modeling to illustrate behavioral aspects of the system (iii) object modeling to represent structural aspects of the system. As a result of our modeling activities we provide the UML diagrams showing various views of the microarray information system. We believe that the object-oriented analysis ensures effective documentations and communication of information system requirements. Another useful feature of object-oriented technique is structural continuity to standard microarray data model MAGE-OM (Microarray Gene Expression Object Model). The proposed modeling e(forts can be applicable for integration of biomedical information system.

• Journal of KIISE:Databases
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• v.34 no.1
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• pp.46-58
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• 2007

Since microarray data acquire tens of thousands of gene expression values simultaneously, they could be very useful in identifying the phenotypes of diseases. However, the results of analyzing several microarray datasets which were independently carried out with the same biological objectives, could turn out to be different. One of the main reasons is attributable to the limited number of samples involved in one microarry experiment. In order to increase the classification accuracy, it is desirable to augment the sample size by integrating and maximizing the use of independently-conducted microarray datasets. In this paper, we propose a novel two-stage approach which firstly integrates individual microarray datasets to overcome the problem caused by limited number of samples, and identifies informative genes, secondly builds a classifier using only the informative genes. The classifier from large samples by integrating independent microarray datasets achieves high accuracy up to 24.19% increase as against other comparison methods, sensitivity, and specificity on independent test sample dataset.

• Genomics & Informatics
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• v.4 no.3
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• pp.110-117
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• 2006

In microarray technology, many diverse experimental features can cause biases including RNA sources, microarray production or different platforms, diverse sample processing and various experiment protocols. These systematic effects cause a substantial obstacle in the analysis of microarray data. When such data sets derived from different experimental processes were used, the analysis result was almost inconsistent and it is not reliable. Therefore, one of the most pressing challenges in the microarray field is how to combine data that comes from two different groups. As the novel trial to integrate two data sets with batch effect, we simply applied standardization to microarray data before the significant gene selection. In the gene selection step, we used new defined measure that considers the distance between a gene and an ideal gene as well as the between-slide and within-slide variations. Also we discussed the association of biological functions and different expression patterns in selected discriminative gene set. As a result, we could confirm that batch effect was minimized by standardization and the selected genes from the standardized data included various expression pattems and the significant biological functions.