• 한국미생물생명공학회:학술대회논문집
• /
• 한국미생물생명공학회 2002년도 학술발표대회
• /
• pp.66-66
• /
• 2002

• 한국영양학회:학술대회논문집
• /
• 한국영양학회 2003년도 연합학술대회
• /
• pp.132.1-132.1
• /
• 2003

• 유전체소식지
• /
• 제6권1호
• /
• pp.12-23
• /
• 2006

• Bioinformatics and Biosystems
• /
• 제1권1호
• /
• pp.28-37
• /
• 2006

Recent studies in molecular biology and proteomics have identified a significant number of novel diagnostic, prognostic, and therapeutic disease markers. However, validation of these markers in clinical specimens with traditional histopathological techniques involves low throughput and is time consuming and labor intensive. Tissue microarrays (TMAs) offer a means of combining tens to hundreds of specimens of tissue onto a single slide for simultaneous analysis. This capability is particularly pertinent in the field of cancer for target verification of data obtained from cDNA micro arrays and protein expression profiling of tissues, as well as in epidemiology-based investigations using histochemical/immunohistochemical staining or in situ hybridization. In combination with automated image analysis, TMA technology can be used in the global cellular network analysis of tissues. In particular, this potential has generated much excitement in cardiovascular disease research. The following review discusses recent advances in the construction and application of TMAs and the opportunity for developing novel, highly sensitive diagnostic tools for the early detection of cardiovascular disease.

• 응용통계연구
• /
• 제22권4호
• /
• pp.781-792
• /
• 2009

유전체 초기단계 연구에서는 비교적 소수의 마이크로어레이 샘플자료로서 실험을 진행하여 심도 깊게 연구해야 할 유전자 부분군(subsets)을 탐색하게 된다. 이러한 과정에서 요구되는 부분군 탐색에 사용되는 분석방법은 다수 샘플자료 분석의 경우와는 매우 다른 방법들이다. 유전자 극소수 샘플자료의 분석에 매우 적절한 방법인 랜덤검정법을 적용하여 정확한 P값(exact P value)의 이산형 분포가 얻어지고, 일양분포 귀무가설의 검정으로 유의 유전자가 존재하는지를 파악할 수 있다. 한 단계 더 나아가 Fuchs와 Kenett (1980)이 제시한 M 검정을 이용하여 이산형 P 값 다항분포에서 이상범주군(outlier cells)을 찾을 수 있으며 이로써 유의 유전자로서의 가능성이 있는 유전자군을 선정한다. 대다수의 마이크로어레이 유전체 연구에서 수 천 또는 수 만개의 유전자가 서로 독립이라고 가정하고 분석하는 것이 문제점이다. 그러나 본 논문에서는 유전자 연관성을 그대로 유지하는 순열에 기초한 랜덤검정법과 M 검정법으로서 유전자 연관성이 분석에 미치는 영향을 모의실험으로 알아보았으며, 그 영향이 결코 미약하지 않음을 확인할 수 있었다.

• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 2012년도 추계학술대회 논문집
• /
• pp.329-330
• /
• 2012

• 한국미생물학회:학술대회논문집
• /
• 한국미생물학회 2005년도 International Meeting of the Microbiological Society of Korea
• /
• pp.197.3-197
• /
• 2005

• 한국지하수토양환경학회:학술대회논문집
• /
• 한국지하수토양환경학회 2005년도 INTERNATIONAL SYMPOSIUM ON SOIL & GROUNDWATER ENVIRONMENT
• /
• pp.85-86
• /
• 2005

• International Journal of Contents
• /
• 제8권1호
• /
• pp.23-29
• /
• 2012

Multiple expression levels of genes obtained using time series microarray experiments have been exploited effectively to enhance understanding of a wide range of biological phenomena. However, the unique nature of microarray data is usually in the form of large matrices of expression genes with high dimensions. Among the huge number of genes presented in microarrays, only a small number of genes are expected to be effective for performing a certain task. Hence, discounting the majority of unaffected genes is the crucial goal of gene selection to improve accuracy for disease diagnosis. In this paper, a non-Gaussian weight matrix obtained from an incremental model is proposed to extract useful features of multivariate time series microarrays. The proposed method can automatically identify a small number of significant features via discovering hidden variables from a huge number of features. An unsupervised hierarchical clustering representative is then taken to evaluate the effectiveness of the proposed methodology. The proposed method achieves promising results based on predictive accuracy of clustering compared to existing methods of analysis. Furthermore, the proposed method offers a robust approach with low memory and computation costs.

• 한국생물정보학회:학술대회논문집
• /
• 한국생물정보시스템생물학회 2006년도 Principles and Practice of Microarray for Biomedical Researchers
• /
• pp.78-78
• /
• 2006

Gene expression analysis can be performed by one-color (intensity-based) or two-color (ratio-based) microarray platforms depending on the specific applications and needs of the researcher. The traditional two-color approach is well founded from a historical and scientific standpoint, and the one-color approach, when paired with high quality microarrays and a robust workflow, offers additional flexibility in experimental design. Two of the major requirements of any microarray platform are system reproducibility, which provides the means for high confidence experiments and accurate comparison across multiple samples; and high sensitivity, for the detection of significant gene expression changes, including small fold changes across multiple gene sets. Each of these requirements is fulfilled by the Agilent One-color Gene Expression Platform as illustrated by the data included in this study. As a result, researchers have the ability to take advantage of the enhanced performance and sensitivity of Agilent's 60-mer oligonucleotide microarrays, and experience the first commercial microarray platform compatible with both one- and two-color detection.