• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.10
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• pp.1924-1929
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• 2007

Nowadays, a lot of related data obtained from these research could be given a new present meaning to accomplish the original purpose of the whole research as a human project. The method of tumor classification based on microarray could contribute to being accurate tumor classification by finding differently expressing gene pattern statistically according to a tumor type. Therefore, the process to select a closely related informative gene with a particular tumor classification to classify tumor using present microarray technology with effect is essential. In this thesis, we used cDNA microarrays of 3840 genes obtained from neuronal differentiation experiment of cortical stem cells on white mouse with cancer, constructed accurate tumor classification model by extracting informative gene list through normalization separately and then did performance estimation by analyzing and comparing each of the experiment results. Result classifying Multi-Perceptron classifier for selected genes using Pearson correlation coefficient represented the accuracy of 95.6%.

• Biomolecules & Therapeutics
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• v.18 no.4
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• pp.411-419
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• 2010

Korean mistletoe has a variety of biological effects, such as immunoadjuvant activities. This study investigates the effects of Korean mistletoe lectin (Viscum album L. var. coloratum agglutinin, VCA) on human T lymphocytes to determine whether VCA acts as an immunomodulator. Purified human T-lymphocytes were cultured with VCA and RNA from each point was analyzed using Affymetrix human genome chips containing 22,500 probe sets which represents more than 18,000 transcripts derived from 14,500 human genes. As a result, there was a striking upregulation of genes coding for chemokines. Seventeen genes out of 50 coding for proteins with chemokine activity were upregulated including CXCL9 and IL-8 which are related to the treatment of cancer. In addition, 28 cytokine genes were upregulated including IL-1, IL-6, IL-8, IFN-$\gamma$, and TNF-$\alpha$. Taken together, the data suggest that Korean mistletoe lectin, in parallel with European mistletoe, has an ability to modulate human T cell function.

• Communications for Statistical Applications and Methods
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• v.13 no.3
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• pp.701-718
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• 2006

In cancer microarray experiments, the experimenter or patient which is nested in each experimenter often shows quite heterogeneous error variability, which should be estimated for identifying a source of variation. Our study describes a Bayesian method which utilizes clinical information for identifying a set of DE genes for the class of subtypes as well as assesses and examines the experimenter effect and patient effect which is nested in each experimenter as a source of variation. We propose a Bayesian multilevel mixed effect model based on analysis of covariance (ANACOVA). The Bayesian multilevel mixed effect model is a combination of the multilevel mixed effect model and the Bayesian hierarchical model, which provides a flexible way of defining a suitable correlation structure among genes.

• Communications for Statistical Applications and Methods
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• v.13 no.1
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• pp.113-123
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• 2006

Normal mixture model(NMM) frequently used to cluster genes on microarray gene expression data. In this paper some of component means of NMM are modelled by a linear regression model so that its design matrix presents the pattern between sample classes in microarray matrix. This modelling for the component means by given design matrices certainly has an advantage that we can lead the clusters that are previously designed. However, it suffers from 'overfitting' problem because in practice genes often are highly dimensional. This problem also arises when the NMM restricted by the linear model for component-means is fitted. To cope with this problem, in this paper, the use of the factor analyzer NMM restricted by linear model is proposed to cluster genes. Also several design matrices which are useful for clustering genes are provided.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.83-86
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• 2006

많은 유전자 정보와 그 부산물은 많은 방법을 통해 연구되어 왔다. DNA 마이크로어레이 기술의 사용은 많은 데이터를 가져왔으며, 이렇게 얻은 데이터는 기존의 연구 방법으로는 분석하기 힘들다. 본 눈문에서는 많은 양의 데이터를 처리할 수 있게 하기 위하여 K-means 클러스터링 알고리즘을 이용한 분할 클러스터링을 제안하였다. 제안한 방법을 쌀 유전자로부터 나온 마이크로어레이 데이터에 적용함으로써 제안된 클러스터링 방법의 유용성을 검증하였으며, 기존의 K-means 클러스터링 알고리즘을 적용한 결과와 비교함으로써 제안된 알고리즘의 우수성을 확인 할 수 있었다.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2002.06a
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• pp.49-61
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• 2002

현대적 실험방법 및 유전공학의 발전으로 최근 생물학적 자료는 비약적으로 늘어나고 있다. 이러한 자료의 기계학습을 이용한 분석방법은 많은 비용과 시간을 요구하는 전통적인 생물적 실험에 있어서 실험 시간을 단축시켜주고 실험비용을 줄여 주게 된다. 본 논문에서는 특별히 micro array data의 분석에 있어서 graphical model에 기반한 기계학습 방법들을 소개한다. 이중 GTM 은 특히 시각화 효과가 뛰어난 방법으로 Graphical model 에 기반한 GTM의 제반 특성을 소개하고 이를 yeast data의 분석에 적용시킨 결과를 자세히 알아보고자 한다. (**Presentation file을 수신 보관 중)

• Journal of the Korea Society of Computer and Information
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• v.20 no.2
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• pp.137-147
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• 2015

This study proposes an algorithm for predicting breast cancer prognosis based on genetic network. We identify prognosis-specific network using gene expression data and PPI(protein-protein interaction) data. To acquire the network, we calculate Pearson's correlation coefficient(PCC) between genes in all PPI pairs using gene expression data. We develop a prediction model for breast cancer patients with estrogen-receptor-negative using the network as a classifier. We compare classification performance of our algorithm with existing algorithms on independent data and shows our algorithm is improved. In addition, we make an functionality analysis on the genes in the prognosis-specific network using GO(Gene Ontology) enrichment validation.

• Genomics & Informatics
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• v.4 no.1
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• pp.23-32
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• 2006

In order to investigate the molecular basis of the aging process in brain, we have employed high-density oligonucleotide microarrays providing data on 10,108 gene clusters to define transcriptional patterns in three brain regions, cerebral cortex, cerebellum, and hippocampus. Comparison of the expression patterns between young (6-week-old) and aged (17-month-old) C57BL/6 male micerevealed that about ten percent (1098) of the genes showed a significant change in the expression level in at least one of the three tissues. Among them, 23 genes were upregulated and 62 genes were downregulated in all three tissues of the old mice. The number of genes upregulated exclusively in hippocampus (337) was much larger compared to other tissues. Gene ontology-based analysis showed the genes related with signal transduction or molecular transports are more likely to be upregulated than downregulated in the aging process of hippocampus. These data may provide some useful means for elucidating the molecular aspect of aging in hippocampus and other regions in brain.

• Genomics & Informatics
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• v.1 no.2
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• pp.94-100
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• 2003

Motivation: Many have observed a nonlinear relationship between the signal intensity and the transcript abundance in microarray data. The first step in analyzing the data is to normalize it properly, and this should include a correction for the nonlinearity. The commonly used linear normalization schemes do not address this problem. Results: Nonlinearity is present in both cDNA and oligonucleotide arrays, but we concentrate on the latter in this paper. Across a set of chips, we identify those genes whose within-chip ranks are relatively constant compared to other genes of similar intensity. For each gene, we compute the sum of the squares of the differences in its within-chip ranks between every pair of chips as our statistic and we select a small fraction of the genes with the minimal changes in ranks at each intensity level. These genes are most likely to be non-differentially expressed and are subsequently used in the normalization procedure. This method is a generalization of the rank-invariant normalization (Li and Wong, 2001), using all available chips rather than two at a time to gather more information, while using the chip that is least likely to be affected by nonlinear effects as the reference chip. The assumption in our method is that there are at least a small number of non­differentially expressed genes across the intensity range. The normalized expression values can be substantially different from the unnormalized values and may result in altered down-stream analysis.

• BMB Reports
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• v.42 no.8
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• pp.493-499
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• 2009

MicroRNAs (miRs) are a family of small noncoding RNAs that regulate gene expression by targeting mRNAs in a sequence specific manner, inducing translational repression or mRNA degradation. In this paper we have first analyzed by microarray the miR-profile in erythroid precursor cells from one normal and two thalassemic patients expressing different levels of fetal hemoglobin (one of them displaying HPFH phenotype). The microarray data were confirmed by RT-PCR analysis, and allowed us to identify miR-210 as an highly expressed miR in the erythroid precursor cells from the HPFH patient. When RT-PCR was performed on mithramycin-induced K562 cells and erythroid precursor cells, miR-210 was found to be induced in time-dependent and dose-dependent fashion, together with increased expression of the fetal $\gamma$-globin genes. Altogether, the data suggest that miR-210 might be involved in increased expression of $\gamma$-globin genes in differentiating erythroid cells.