• Title, Summary, Keyword: microarray

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Applications and Developmental Prospect of Protein Microarray Technology (Protein Microarray의 응용 및 발전 전망)

  • Oh, Young-Hee;Han, Min-Kyu;Kim, Hak-Sung
    • KSBB Journal
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    • v.22 no.6
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    • pp.393-400
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    • 2007
  • Analysis of protein interactions/functions in a microarray format has been of great potential in drug discovery, diagnostics, and cell biology, because it is amenable to large-scale and high-throughput biological assays in a rapid and economical way. In recent years, the protein microarray have broaden their utility towards the global analysis of protein interactions on a proteome scale, the functional activity analysis based on protein interactions and post-translational modifications (PTMs), and the discovery of biomarkers through profiling of protein expression between sample and reference pool. As a promising tool for proteomics, the protein microarray technology has advanced outstandingly over the past decade in terms of surface chemistry, acquisition of relevant proteins on a proteomic level, and detection methods. In this article, we briefly describe various techniques for development of protein microarray, and introduce developmental state of protein microarray and its applications.

Development of DNA Chip Microarray Using Hydrophobic Template (소수성 Template를 이용한 DNA Chip Microarray의 개발)

  • Choi, Yong-Sung;Park, Dae-Hee
    • Proceedings of the KIEE Conference
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    • pp.271-274
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    • 2004
  • Microarray-based DNA chips provide an architecture for multi-analyte sensing. In this paper, we report a new approach for DNA chip microarray fabrication. Multifunctional DNA chip microarray was made by immobilizing many kinds of biomaterials on transducers (particles). DNA chip microarray was prepared by randomly distributing a mixture of the particles on a chip pattern containing thousands of m-scale sites. The particles occupied a different sites from site to site. The particles were arranged on the chip pattern by the random fluidic self-assembly (RFSA) method, using a hydrophobic interaction for assembly.

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Microarray Data Sharing System (마이크로어레이 데이터 공유 시스템)

  • Yoon, Jee-Hee;Hong, Dong-Wan;Lee, Jong-Keun
    • 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.

Good to Great Microarray Research

  • Kim Seong-Han
    • Proceedings of the Korean Society for Bioinformatics Conference
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    • pp.57-61
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    • 2006
  • Microarray란 유리, 실리콘, 플라스틱 등의 매체위에 생체분자를 집적하여 만든 플랫폼을 의미한다. 현재 이러한 플랫폼에 DNA, 화학물질, 유기물질 등 바이오소재를 집적하여 다양한 연구용 제품들이 출시되어 있으며, 수년간 Microarray를 이용한 연구가 진행되어 최근에는 질병진단/예후예측 등의 포괄적인 정보를 포함하는 임상용 microarray제품도 등장하고 있다. 디지탈지노믹스(주)는 2000년 이후로 6년의 기간동안 연구자에게 다양한 종류의microarray를 공급하여 왔으며, 현재 국내에서 가장 많은 종류의 microanay 분석 시스템을 확보하고 있다. 따라서 다양한 연구자들에게 가장 적합한 microarray를 소개할 수 있음은 물론, 그 결과분석 데이터를 제공함으로써 양질의 데이터와 서비스를 제공하고 있다. 특히 디지탈지노믹스(주)에서는 최근에 Combimatrix사의 microarray 시스템을 도입하여, 연구자가 원하는 맞춤형 microarray를 제작할 수 있는 새로운 형태의 차세대 플랫폼을 제공할 수 있게 되었다. 이 기술은 연구자의 목적에 맞게 microarray 제작이 가능하도록 가변적인 특성을 가지고 있으며 높은 민감도 및 재현성을 보여주는 우수한 기술력을 보여준다. Microarray 분야는 그 플랫폼과 분석기술이 나날이 발전하고 있으며, 그 응용범위도 날로 넓어 지고 있다. 그 활용범위의 예를 보면, 1) 유전체 수준에서 발현양상 분석, 2) 약물에 대한 반응성 분석, 3) 질환에 대한 원인 유전자 규명 및 진단제 개발, 4) 독성유전체에서의 약효 및 유효성 분석, 5) 대량의 SNP 분석, 6) 대량의 단백질 수준에서의 발현분석 등이 있으며, 일일이 다 언급하기 힘들 정도로 그 응용범위가 넓어지고 있다. 이러한 microarray기술은 관심 있는 대상에 대한 검색(screening)의 기능과 더불어 분석된 데이터를 기초로 제품화 플랫폼으로써 다시 활용될 수 있는 장점을 가지고 있다. 디지탈지노믹스(주)에서는 구축되어 있는 microarray 분석 시스템을 이용하여 질병 진단, 약물반응성 진단 및 플랫폼 개발에 대한 내부연구도 심도 있게 수행하고 있으며, microarray 기술을 응용하여 산업화, 제품화 할 수 있는 구체적인 사례와 모범답안을 만들기 위해 노력하고 있다.

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Network-based Microarray Data Analysis Tool

  • Park, Hee-Chang;Ryu, Ki-Hyun
    • 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.

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Large-Circular Single-stranded Sense and Antisense DNA for Identification of Cancer-Related Genes (장환형 단일가닥 DNA를 이용한 암세포 성장 억제 유전자 발굴)

  • Bae, Yun-Ui;Moon, Ik-Jae;Seu, Young-Bae;Doh, Kyung-Oh
    • Microbiology and Biotechnology Letters
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    • v.38 no.1
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    • pp.70-76
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    • 2010
  • The single-stranded large circular (LC)-sense DNA were utilized as probes for DNA chip experiments. The microarray experiment using LC-sense DNA probes found differentially expressed genes in A549 cells as compared to WI38VA13 cells, and microarray data were well-correlated with data acquired from quantitative real-time RT-PCR. A 5K LC-sense DNA microarray was prepared, and the repeated experiments and dye swap test showed consistent expression patterns. Subsequent functional analysis using LC-antisense library of overexpressed genes identified several genes involved in A549 cell growth. These experiments demonstrated proper feature of LC-sense molecules as probe DNA for microarray and the potential utility of the combination of LC-sense microarray and antisense libraries for an effective functional validation of genes.

The Application of Machine Learning Algorithm In The Analysis of Tissue Microarray; for the Prediction of Clinical Status

  • Cho, Sung-Bum;Kim, Woo-Ho;Kim, Ju-Han
    • Proceedings of the Korean Society for Bioinformatics Conference
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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.

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Metastasis Related Gene Exploration Using TwoStep Clustering for Medulloblastoma Microarray Data

  • Ban, Sung-Su;Park, Hee-Chang
    • 한국데이터정보과학회:학술대회논문집
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    • pp.153-159
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    • 2005
  • Microarray gene expression technology has applications that could refine diagnosis and therapeutic monitoring as well as improve disease prevention through risk assessment and early detection. Especially, microarray expression data can provide important information regarding specific genes related with metastasis through an appropriate analysis. Various methods for clustering analysis microarray data have been introduced so far. We used twostep clustering fot ascertain metastasis related gene through t-test. Through t-test between two groups for two publicly available medulloblastoma microarray data sets, we intended to find significant gene for metastasis. The paper describes the process in detail showing how the process is applied to clustering analysis and t-test for microarray datasets and how the metastasis-associated genes are explorated.

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Comparison of methods for the proportion of true null hypotheses in microarray studies

  • Kang, Joonsung
    • Communications for Statistical Applications and Methods
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    • v.27 no.1
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    • pp.141-148
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    • 2020
  • We consider estimating the proportion of true null hypotheses in multiple testing problems. A traditional multiple testing rate, family-wise error rate is too conservative and old to control type I error in multiple testing setups; however, false discovery rate (FDR) has received significant attention in many research areas such as GWAS data, FMRI data, and signal processing. Identify differentially expressed genes in microarray studies involves estimating the proportion of true null hypotheses in FDR procedures. However, we need to account for unknown dependence structures among genes in microarray data in order to estimate the proportion of true null hypothesis since the genuine dependence structure of microarray data is unknown. We compare various procedures in simulation data and real microarray data. We consider a hidden Markov model for simulated data with dependency. Cai procedure (2007) and a sliding linear model procedure (2011) have a relatively smaller bias and standard errors, being more proper for estimating the proportion of true null hypotheses in simulated data under various setups. Real data analysis shows that 5 estimation procedures among 9 procedures have almost similar values of the estimated proportion of true null hypotheses in microarray data.

Development of a Reproducibility Index for cDNA Microarray Experiments

  • Kim, Byung-Soo;Rha, Sun-Young
    • Proceedings of the Korean Statistical Society Conference
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    • pp.79-83
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    • 2002
  • Since its introduction in 1995 by Schena et al. cDNA microarrays have been established as a potential tool for high-throughput analysis which allows the global monitoring of expression levels for thousands of genes simultaneously. One of the characteristics of the cDNA microarray data is that there is inherent noise even after the removal of systematic effects in the experiment. Therefore, replication is crucial to the microarray experiment. The assessment of reproducibility among replicates, however, has drawn little attention. Reproducibility may be assessed with several different endpoints along the process of data reduction of the microarray data. We define the reproducibility to be the degree with which replicate arrays duplicate each other. The aim of this note is to develop a novel measure of reproducibility among replicates in the cDNA microarray experiment based on the unprocessed data. Suppose we have p genes and n replicates in a microarray experiment. We first develop a measure of reproducibility between two replicates and generalize this concept for a measure of reproducibility of one replicate against the remaining n-1 replicates. We used the rank of the outcome variable and employed the concept of a measure of tracking in the blood pressure literature. We applied the reproducibility measure to two sets of microarray experiments in which one experiment was performed in a more homogeneous environment, resulting in validation of this novel method. The operational interpretation of this measure is clearer than Pearson's correlation coefficient which might be used as a crude measure of reproducibility of two replicates.

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