• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.2
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• pp.192-197
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• 2012

With the advent of DNA microarray technologies, researches for disease diagnosis has been actively in progress. In typical experiments using microarray data, problems such as the large number of genes and the relatively small number of samples, the inherent measurement noise and the heterogeneity across different samples are the cause of the performance decrease. To overcome these problems, a new method using functional modules (e.g. signaling pathways) used as markers was proposed. They use the method using an activity of pathway summarizing values of a member gene's expression values. It showed better classification performance than the existing methods based on individual genes. The activity calculation, however, used in the method has some drawbacks such as a correlation between individual genes and each phenotype is ignored and characteristics of individual genes are removed. In this paper, we propose a method based on the ensemble classifier. It makes weak classifiers based on feature vectors using subsets of genes in selected pathways, and then infers the final classification result by combining the results of each weak classifier. In this process, we improved the performance by minimize the search space through a filtering process using gene-gene interaction information and the mRMR filter. We applied the proposed method to a classifying the lung cancer, it showed competitive classification performance compared to existing methods.

• The KIPS Transactions:PartD
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• v.14D no.1 s.111
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• pp.9-20
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• 2007

Groups of genes control the functioning of a cell by complex interactions. Such interactions of gene groups are tailed Gene Regulatory Networks(GRNs). Two previous data mining approaches, clustering and classification, have been used to analyze gene expression data. Though these mining tools are useful for determining membership of genes by homology, they don't identify the regulatory relationships among genes found in the same class of molecular actions. Furthermore, we need to understand the mechanism of how genes relate and how they regulate one another. In order to detect regulatory relationships among genes from time-series Microarray data, we propose a novel approach using frequent pattern mining and chain rules. In this approach, we propose a method for transforming gene expression data to make suitable for frequent pattern mining, and gene expression patterns we detected by applying the FP-growth algorithm. Next, we construct a gene regulatory network from frequent gene patterns using chain rules. Finally, we validate our proposed method through our experimental results, which are consistent with published results.

• The Korean Journal of Applied Statistics
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• v.27 no.3
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• pp.397-406
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• 2014

The minimum density power divergence estimation has been a popular topic in the field of robust estimation for since Basu et al. (1988). The minimum density power divergence estimator has strong robustness properties with the little loss in asymptotic efficiency relative to the maximum likelihood estimator under model conditions. However, a limitation in applying this estimation method is the algebraic difficulty on an integral involved in an estimation function. This paper considers a minimum density power divergence estimation method with approximated divergence avoiding such difficulty. As an example, we consider the normal-exponential convolution model introduced by Bolstad (2004). The estimated divergence in this case is too complicated; consequently, a Laplace approximation is employed to obtain a manageable form. Simulations and an empirical study show that the minimum density power divergence estimators based on an approximated estimated divergence for the normal-exponential model perform adequately in terms of bias and efficiency.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.4
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• pp.27-33
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• 2008

The clustering of gene expression data is used to analyze the results of microarray studies. This clustering is one of the frequently used methods in understanding degrees of biological change and gene expression. In biological research, MCL algorithm is an algorithm that clusters nodes within a graph, and is quick and efficient. We have modified the existing MCL algorithm and applied it to microarray data. In applying the MCL algorithm we put forth a simulation that adjusts two factors, namely inflation and diagonal tent and converted them by making use of Markov matrix. Furthermore, in order to distinguish class more clearly in the modified MCL algorithm we took the average of each row and used it as a threshold. Therefore, the improved algorithm can increase accuracy better than the existing ones. In other words, in the actual experiment, it showed an average of 70% accuracy when compared with an existing class. We also compared the MCL algorithm with the self-organizing map(SOM) clustering, K-means clustering and hierarchical clustering (HC) algorithms. And the result showed that it showed better results than ones derived from hierarchical clustering and K-means method.

• Journal of Life Science
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• v.17 no.5 s.85
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• pp.664-670
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• 2007

The propolis is natural product produced by honeybees and is known to have many biologically useful properties such as anti-microbial, anti-oxidative and anti-tumorigenic activity. However, its radio-protective property has not been well studied. To investigate radio-protective effect of propolis on mouse testis, mice were supplemented with propolis after 5 Gy irradiation. The histological changes of testis were detected by TEM. The results indicate that propolis may protect tissue deformation which is induced by 5 Gy of ionizing radiation. Furthermore, to elucidate the potential molecular mechanisms involved in radio-protective property of propolis, we performed microarray experiments using oligo DNA microarray. We found 65 up-regulated genes and 224 down-regulated genes, whose expression levels were affected more than 2-fold by propolis treatment in mice irradiated at 5 Gy. We confirmed microarray data with reverse transcription-PCR using gene specific primers. The results of RT-PCR are highly correlated with those of microarray. These results may help understanding molecular mechanisms of radioprotective effects by propolis in mouse model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.12
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• pp.2259-2264
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• 2008

Significant genes are defined as genes in which the expression level characterizes a specific experimental condition. Such genes in which the expression levels differ significantly between different groups are highly informative relevant to the studied phenomenon. In this paper, first the system can detect informative genes by similarity scale combination method being proposed in this paper after normalizing data with methods that are the most widely used among several normalization methods proposed the while. And it compare and analyze a performance of each of normalization methods with multi-perceptron neural network layer. The Result classifying in Multi-Perceptron neural network classifier for selected 200 genes using combination of PC(Pearson correlation coefficient) and ED(Euclidean distance coefficient) after Lowess normalization represented the improved classification performance of 98.84%.

• Journal of Life Science
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• v.19 no.12
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• pp.1787-1793
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• 2009

To investigate whether caffeic acid phenethyl ester (CAPE) could affect cancer cell viabilities and gene expression, human colorectal HCT116 cells were incubated with CAPE. CAPE decreased cancer cell viabilities and induced apoptosis in a dose-dependent manner. To analyse differently expressed genes by CAPE, we performed oligo DNA microarray analysis. We found that 266 genes were up-regulated more than twofold, whereas 143 genes were down-regulated more than twofold by 24 hr of treatment with $20{\mu}M$ CAPE. Among the up-regulated genes, we selected 3 genes (NSAID activated gene-1 [NAG-1], cyclin-dependent kinase inhibitor 1A [CDKN1A, p21] and growth arrest and DNA-damage-inducible alpha [GADD45A]) and performed reverse-transcription PCR to confirm microarray data. In addition, we found that CAPE increased NAG-1 gene and NAG-1 protein expression in a dose-dependent manner. And, several other phytochemicals (resveratrol, genistein, daidzein and capsaicin) also could induce NAG-1 expression in human colorectal HCT116 cells. However, CAPE was the highest inducer of NAG-1, even in low concentrations. Overall, these results imply that cancer cell death by CAPE is closely related with over-expression of NAG-1.

• Journal of Life Science
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• v.33 no.1
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• pp.64-72
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• 2023

Despite several advances in identification of cardiac transcription factors, there are still needs to find new bioactive molecules that promote cardiomyogenesis from stem cells to highly efficient myocardial differentiation. We analyzed Illumina expression microarray data of mouse embryonic stem cells (mESCs)-derived cardiomyocytes. 276 genes were upregulated (≥ 4fold) in mESCs-derived cardiomyocytes compared undifferentiated ESCs. Secreted phosphoprotein 2 (Spp2) is one of candidates and is known to inhibit bone morphogenetic protein 2 (BMP2) signal transduction as a pseudoreceptor for BMP2. However, its function in cardiomyogenesis is unknown. We confirmed that Spp2 expression increased during the differentiation into functional cardiomyocytes using mESCs, TC-1/Kh2 and E14. Interestingly, Spp2 secretion transiently increased 3 days after formation of embryoid bodies (EBs), indicating that the extracellular secretion of Spp2 is involved in the differentiation of ESCs into cardiomyocytes. To characterize Spp2, we performed experiments using the C2C12 mouse myoblast cell line, which has the property of shifting the differentiation pathway from myoblastic to osteoblastic by treatment with BMP2. Similar to the differentiation of ESCs, transcription of Spp2 increased as C2C12 myoblasts differentiated into myotubes. In particular, Spp2 secretion increased dramatically in the early stage of differentiation. Furthermore, treatment with Spp2-Flag recombinant protein promoted the differentiation of C2C12 myoblasts into myotubes. Taken together, we suggest a novel bioactive protein Spp2 that differentiates ESCs into cardiomyocytes. This may be useful for understanding the molecular pathways of cardiomyogenesis and for experimental or clinical promotion of stem cell therapy for ischemic heart diseases.

• Journal of Life Science
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• v.22 no.1
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• pp.49-54
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• 2012

The effects of genistein on cell proliferation and adipogenesis were examined in mouse 3T3-L1 preadipocyte cells. Genistein decreased viability of 3T3-L1 pre-adipocytes in a dose-dependent manner. Oil Red O staining of these cells also indicated that adipogenesis was inhibited by 50 ${\mu}M$ genistein treatment. We investigated the molecular mechanisms involved in the decrease in cell viability in genistein-treated 3T3-L1 cells by conducting an oligo DNA microarray analysis. We selected the sirtuin-1 gene, one of the upregulated genes, for further experimentation because sirtuin-1 belongs to the sirtuin family, which is associated with anti-obesity and anti-inflammation activities. We found that four phytochemicals (resveratrol, capsaicin, daidzein, and genistein) could increase sirtuin-1 expression. Genistein was the strongest inducer of sirtuin-1 among the tested phytochemicals. The inhibition of adipogenesis by genistein was recovered by surtuin-1 siRNA transfection. Overall, these results may further our understanding of the molecular mechanisms underlying the inhibition of proliferation and adipogenesis by genistein in mouse 3T3-L1 cells.

• Horticultural Science & Technology
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• v.31 no.5
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• pp.607-616
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• 2013

Light with two faces, beneficial and harmful effects is an important signal for every living cell. Optimal adaptation to light environment enhances the fitness of an organism and survival in nature. Understandings of light quality and plant growth provide with the economical guides for artificial light sources like LEDs. Compared with those under white light, the 1 week seedlings of Chinese cabbage (Brassica rapa) under monochromic red and blue light showed normal development and growth. In contrast to extremely long and etiolated hypocotyls of the seedlings under dark, those under far-red etiolated were extremely short. Based on the microarray analysis, blue light induced the vigorous development and growth and two fold changes of transcripts than red light condition. To have insight of gene products under different light qualities conditions, GO term enrichments were calculated and each gene according to their GO terms were categorized. The blue and red lights affected the expressions of genes related to biological process. Especially, the genes related to metabolic process and developmental process and plastid and chloroplast in the cellular component category were induced under blue light. This study provided the molecular biological evidence for various light qualities on the growing process of B. rapa.