• Proceedings of the Korean Society for Bioinformatics Conference
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• 2004.11a
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• pp.124-130
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• 2004

Microarray expression datasets are incessantly cumulated with the aid of recent technological advances. One of the first steps for analyzing these data under various experimental conditions is determining differentially expressed genes (DEGs) in each condition. Reasonable choices of thresholds for determining differentially expressed genes are used for the next -step-analysis with suitable statistical significances. We present a model for identifying DEGs using pathway information based on the global connectivity structure. Pathway information can be regarded as a collection of biological knowledge, thus we are tying to determine the optimal threshold so that the consequential connectivity structure can be the most compatible with the existing pathway information. The significant feature of our model is that it uses established knowledge as a reference to determine the direction of analyzing microarray dataset. In the most of previous work, only intrinsic information in the miroarray is used for the identifying DEGs. We hope that our proposed method could contribute to construct biologically meaningful network structure from microarray datasets.

• International Journal of Contents
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• v.8 no.1
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• pp.23-29
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• 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.

• Genomics & Informatics
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• v.5 no.3
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• pp.113-117
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• 2007

The development of microarray technology has facilitated the understanding of gene expression profiles. Despite its convenience, the cause of dye-bias that confounds data interpretation in dual-color DNA microarray experiments is not well known. In order to economize time and money, it is necessary to identify the cause of dye bias, since designing dye-swaps to reduce the dye-specific bias tends to be very expensive. Hence, we sought to determine the reliable cause of systematic dye bias after treating murine macrophage RAW 264.7 cells with 2-keto-3-deoxyoctonate (KDO), interferon-beta $(IFN-{\beta})$, and 8-bromoadenosine (8-BR). To find the cause of systematic dye bias from the point of view of fluorescence quenching, we examined the correlation between systematic dye bias and the proportion of each nucleotide in mRNA and oligonucleotide probe sequence. Cy3-dye bias was highly correlated with the proportion of adenines. Our results support the fact that systematic dye bias is affected by fluorescence quenching of each feature. In addition, we also found that the strength of fluorescence quenching is based on not only dye-dye interactions but also dye-nucleotide interactions as well.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2006.02a
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• pp.78-78
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• 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.

• Proceedings of the Korean Society of Sericultural Science Conference
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• 2003.04a
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• pp.47-48
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• 2003

The development of cDNA microarray has permitted the analysis of thousands of genes simultaneously. cDNA microarray has been used to analyze gene expression profiles during developmental stage in both single and multicellular organisms. Two significant factors contributing to the limitation of the development of cDNA microarray in the Bombyx mori are the shortage of accessible repositories of cDNA clones and ESTs and the relative scarcity of facilities to produce microarrays and analyze the data generated. (omitted)

• Journal of Pharmacopuncture
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• v.11 no.4
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• pp.5-14
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• 2008

Objective This study was undertaken to determine the effect of Whallak-tang on expression of CD/cytokine Genes. Methods The expression of CD/Cytokine Genes were examined by cDNA microarray using the human mast cell line(HMC-1). Results The expression of ATP5F1, FLJ20671, unknown, KIAA0342, OAS2, unknown genes were increased in $200{\sim}300%$ range. The expression of unknown, MDS006, IFITM1, MRPL3, ZNF207, FTH1, FBP1, NRGN, NR1H2, KIAA0747 genes were decreased in $0{\sim}33%$ range. Conclusion These results would provide important basic data on the possibility of the clinical treatment of Whallak-tang in musculoskeletal disease.

• ETRI Journal
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• v.41 no.3
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• pp.358-370
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• 2019

The main aim of this study is to select the optimal set of genes from microarray cancer datasets that contribute to the prediction of specific cancer types. This study proposes the enhancement of the feature selection filter algorithm based on Joe's normalized mutual information and its use for gene selection. The proposed algorithm is implemented and evaluated on seven benchmark microarray cancer datasets, namely, central nervous system, leukemia (binary), leukemia (3 class), leukemia (4 class), lymphoma, mixed lineage leukemia, and small round blue cell tumor, using five well-known classifiers, including the naive Bayes, radial basis function network, instance-based classifier, decision-based table, and decision tree. An average increase in the prediction accuracy of 5.1% is observed on all seven datasets averaged over all five classifiers. The average reduction in training time is 2.86 seconds. The performance of the proposed method is also compared with those of three other popular mutual information-based feature selection filters, namely, information gain, gain ratio, and symmetric uncertainty. The results are impressive when all five classifiers are used on all the datasets.

• Journal of fish pathology
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• v.36 no.2
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• pp.191-211
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• 2023

Rock bream iridovirus (RBIV) causes high mortality and economic losses in rock bream (Oplegnathus fasciatus) aquaculture industry in Korea. Although, the immune responses of rock bream under RBIV infection have been studied, there is not much information at the different stages of infection (initial, middle and recovery). Gene expression profiling of rock bream under different RBIV infection stages was investigated using a microarray approaches. In total, 5699 and 6557 genes were significantly up- or down-regulated over 2-fold, respectively, upon RBIV infection. These genes were grouped into categories such as innate immune responses, adaptive immune responses, complements, lectin, antibacterial molecule, stress responses, DNA/RNA binding, energy metabolism, transport and cell cycle. Interestingly, hemoglobins (α and β) appears to be important during pathogenesis; it is highly up-regulated at the initial stage and is gradually decreased when the pathogen most likely multiplying and fish begin to die at the middle or later stage. Expression levels were re-elevated at the recovery stage of infection. Among up-regulated genes, interferon-related genes were found to be responsive in most stages of RBIV infection. Moreover, X-linked inhibitor of apoptosis (XIAP)-associated factor 1 (XAF1) expression was high, whereas expression of apoptosis-relate genes were low. In addition, stress responses were highly induced in the virus infection. The cDNA microarray data were validated using quantative real-time PCR. Our results provide novel inslights into the broad immune responses triggered by RBIV at different infection stages.

• Reproductive and Developmental Biology
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• v.32 no.4
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• pp.229-235
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• 2008

Silencing of Dicer1 by siRNA did not inhibit development up to the blastocyst stage, but decreased expression of selected transcription factors, including Oct-4, Sox2 and Nanog, suggesting that Dicer1 gene expression is associated with differentiation processes at the blastocyst stage (Cui et al., 2007). In order to get insights into genes which may be linked with microRNA system, we compared gene expression profiles in Gapdh and Dicer1 siRNA-microinjected blastocysts using the Applied Biosystem microarray technology. Our data showed that 397 and 737 out of 16354 genes were up- and down-regulated, respectively, following siRNA microinjection (p<0.05), including 24 up- and 28 down-regulated transcription factors. Identification of genes that are preferentially expressed at particular Dicer1 knock down embryos provides insights into the complex gene regulatory networks that drive differentiation processes in embryos at blastocyst stage.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2006.02a
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• pp.20-28
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• 2006

최근의 microarray 기술의 발달로 인해 점점 더 많은 양의 mRNA 발현 데이터가 쌓여 가고 있다. 이제는 데이터를 만드는 단계보다는 데이터로부터 중요한 생물학적 의미를 끌어내는 것이 더욱 중요한 일이 되었다. micorarray 기술이 처음 도입된 이후로, 많은 앨고리즘과 소프트웨어가 개발되어, 실험자들이 microarray 데이터로부터 생물학적 의미를 끌어내는 작업을 도와주어 왔다. 그런데, 이전의 데이터 마이닝 방법들은 거의 예외 없이 전체 데이터로부터 선택된 몇 십, 몇 백 개의 유전자 리스트로부터 출발한다. 그런데, 이러한 방법 (over-representation analysis, ORA로 줄임)은 몇 가지 한계를 가지고 있어서, 최근에는 전체 데이터로부터 의미 있는 유전자 세트 (gene set)를 찾아내는 방법들이 도입되었다. 본 세미나는 이런 방법들, 줄여서 gene set analysis라 함, 에 사용되는 앨고리즘들과 소프트웨어들을 비교, 검토하고자 한다.