• BMB Reports
• /
• v.37 no.2
• /
• pp.148-152
• /
• 2004

A new molecular-baiting method was studied by retrieving targeted gene fragments from an oligonucleotide microarray bait after hybridization. To make the microarray bait, 70-mer oligonucleotides that were designed to specifically represent the SSA1 gene of Saccharomyces cerevisiae were printed on the slide. Samples of the Saccharomyces cerevisiae mRNA were extracted and labeled by the RD-PCR (Restriction Display PCR) method using the Cy5-labelled universal primer, then applied for hybridization. The sample fragments that hybridized to the microarray were stripped, and the eluted cDNAs were retrieved and cloned into the pMD 18-T vector for transformation, plasmid preparation, and sequencing. BLAST searching of the GenBank database identified the retrieved fragments as being identical to the SSA1 gene (from 2057-2541bp). A new method is being established that can retrieve the sample fragments using an oligo-microarray-bait.

• Journal of Institute of Control, Robotics and Systems
• /
• v.11 no.10
• /
• pp.830-836
• /
• 2005

Application of microarray technologies which monitor simultaneously the expression pattern of thousands of individual genes in different biological systems results in a tremendous increase of the amount of available gene expression data and have provided new insights into gene expression during drug development, within disease processes, and across species. There is a great need of data mining methods allowing straightforward interpretation, visualization and analysis of the relevant information contained in gene expression profiles. Specially, classifying biological samples into known classes or phenotypes is an important practical application for microarray gene expression profiles. Gene expression profiles obtained from tissue samples of patients thus allowcancer classification. In this research, molecular classification of microarray gene expression data is applied for multi-class cancer using computational biology such gene selection, principal component analysis and fuzzy clustering. The proposed method was applied to microarray data from leukemia patients; specifically, it was used to interpret the gene expression pattern and analyze the leukemia subtype whose expression profiles correlated with four cases of acute leukemia gene expression. A basic understanding of the microarray data analysis is also introduced.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2007.11a
• /
• pp.149-152
• /
• 2007

본 논문은 바이오 인포메틱스의 분야를 간단히 소개하고 기능유전체학에서 microarray 실험에 대한 통계적 방법론을 살펴보고자 한다. 또한 DNA chip 설계와 생물학적 특정에 대해 살펴보고 각 분야에서 적용되는 통계적 방법을 연구분석 해보고자 한다.

• Proceedings of the Korean Statistical Society Conference
• /
• 2003.10a
• /
• pp.331-334
• /
• 2003

마이크로 어레이(microarray)실험에서 표준화(normalization)는 유전자의 발현수준에 영향을 미치는 여러 기술적인 변인을 제거하는 과정이다. cDNA microarray normalization에 있어 여러 방법이 제안되었지만, 이중 print-tip 효과가 존재할 때 사용되는 방법으로 print-tip lowess normalization이 대표적으로 사용된다. normalization에 사용되는 lowess 함수는 데이터의 특성에 따라 window width를 정해야만 연구의 목적에 맞는 결과를 도출할 수 있다. 본 논문에서는 각각의 tip에서 최적의 window width를 계산하는 절차를 논의하였다. 또한 이의 결과와 기존의 같은 window width를 사용하는 print-tip lowess normalization 결과와 비교 평가하여 normalization의 기본 원칙에 대한 타당성을 확인하였다.

• Communications for Statistical Applications and Methods
• /
• v.11 no.1
• /
• pp.59-77
• /
• 2004

Since the introduction of DNA microarray, a revolutionary high through-put biological technology, a lot of papers have been published to deal with the analyses of the gene expression data from the microarray. In this paper we review most papers relevant to the cDNA microarray data, classify them in statistical methods' point of view, and present some statistical methods deserving consideration and future study.

• Proceedings of the Zoological Society Korea Conference
• /
• 2001.10a
• /
• pp.253.2-253
• /
• 2001

No Abstract, See Full Text

• Communications for Statistical Applications and Methods
• /
• v.13 no.1
• /
• pp.101-111
• /
• 2006

One of the most widely used method of handling missingness in microarray data is the kNN(k Nearest Neighborhood) method. Recently Li and Gui (2004) suggested, so called PCR(Partial Cox Regression) method which deals with censored survival times and microarray data efficiently via kNN imputation method. In this article, we try to show that the way to treat missingness eventually affects the further statistical analysis.

• Proceedings of the Korean Statistical Society Conference
• /
• 2005.11a
• /
• pp.37-41
• /
• 2005

Exploratory data analysis(EDA) is the initial stage of data analysis and provides a useful overview about the whole microarray experiment. If the experiments are replicated, the analyst should check the quality and reliability of microarray data within same experimental condition before the deeper statistical analysis. We shows EDA method focusing on the quality and reproducibility for replicates.

• Genomics & Informatics
• /
• v.5 no.3
• /
• pp.129-132
• /
• 2007

arraylmpute is a software for exploratory analysis of missing data and imputation of missing values in microarray data. It also provides a comparative analysis of the imputed values obtained from various imputation methods. Thus, it allows the users to choose an appropriate imputation method for microarray data. It is built on R and provides a user-friendly graphical interface. Therefore, the users can easily use arraylmpute to explore, estimate missing data, and compare imputation methods for further analysis.

• YAKHAK HOEJI
• /
• v.51 no.3
• /
• pp.179-185
• /
• 2007

Gastric cancer is one of the most common malignancies in Korea, but the predominant molecular event underlying gastric carcinogenesis remain unknown. Recently, DNA microarray technology has enabled the comprehensive analysis of gene expression level, and as such has yielded great insight into the molecular nature of cancer, However, despite the powerful approach of this techniques, the technical artifacts and/or bias in applied array platform limited the liability of resultant tens of thousand data points from microarray experiments. Therefore, we applied two different any platforms, such as olignucleotide microarray and cDNA microarray, to identify gastric cancer related large-scale molecular signature of the same human specimens. When thirty sets of matched human gastric cancer and normal tissues subjected to oligonucleotide microarray, total 623 genes were resulted as differently expressed genes in gastric cancer compared to normal tissues, and 252 genes for cDNA microarray analysis. In addition, forty three outlier genes which reflect the characteristic expression signature of gastric cancer beyond array platform and analytical protocol was recapitulated from two different expression profile. In conclusion, we were able to identify robust large-scale molecular changes in gastric cancer by applying two different platform of DNA microarray, this may facilitate to understand molecular carcinogenesis of gastric cancer.