• Journal of the Korean Data and Information Science Society
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• v.16 no.4
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• pp.951-958
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• 2005

As a foundation study of stem cell applied research, it is necessary to identify the large gene expression through cDNA microarray to understand principles of the level of molecular about cell function. In this paper, we investigated the gene expression through the K-means clustering method and path analysis with genes related to pluripoteny and differentiation in an mouse early stage embryonic development process and embryonic stem cell differentiation. We find a few biological phenomenon through this study. Also, we realize that this process provides functional relationship of unknown genes.

• Communications for Statistical Applications and Methods
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• v.25 no.5
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• pp.559-568
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• 2018

Gene classification can involve complex order-restricted inference. Examining gene expression pattern across groups with order-restriction makes standard statistical inference ineffective and thus, requires different methods. For this problem, Roy's union-intersection principle has some merit. The M-estimator adjusting for outlier arrays in a microarray study produces a robust test statistic with distribution-insensitive clustering of genes. The M-estimator in conjunction with a union-intersection principle provides a nonstandard robust procedure. By exact permutation distribution theory, a conditionally distribution-free test based on the proposed test statistic generates corresponding p-values in a small sample size setup. We apply a false discovery rate (FDR) as a multiple testing procedure to p-values in simulated data and real microarray data. FDR procedure for proposed test statistics controls the FDR at all levels of ${\alpha}$ and ${\pi}_0$ (the proportion of true null); however, the FDR procedure for test statistics based upon normal theory (ANOVA) fails to control FDR.

• Communications for Statistical Applications and Methods
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• v.13 no.3
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• pp.567-576
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• 2006

In this paper, we applied the multiblock dimension reduction methods to the classification of tumor based on microarray gene expressions data. This procedure involves clustering selected genes, multiblock dimension reduction and classification using linear discrimination analysis and quadratic discrimination analysis.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2001.10a
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• pp.97-115
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• 2001

cDNA microarray technology allows the monitoring of expression levels for thousands of genes simultaneously. Many statistical analysis tools become widely applicable to the analysis of cDNA microarray data. In this talk, we consider a two-way ANOVA model to differentiate genes that have high variability and ones that do not. Using this model, we detect genes that have different gene expression profiles among experimental groups. The two-way ANOVA model is illustrated using cDNA microarrays of 3,800 genes obtained in an experiment to search for changes in gene expression profiles during neuronal differentiation of cortical stem cells.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2006.02a
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• pp.71-76
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• 2006

Comparative genomic hybridization (CGH) is a technique for studying chromosomal changes in cancer. As cancerous cells multiply, they can undergo dramatic chromosomal changes, including chromosome loss, duplication, and the translocation of DNA from one chromosome to another. Chromosome aberrations have previously been detected using optical imaging of whole chromosomes, a technique with limited sensitivity, resolution, quantification, and throughput. Efforts in recent years to use microarrays to overcome these limitations have been hampered by inadequate sensitivity, specificity and flexibility of the microarray systems. The oligonucleotide CGH microarray system overcomes several scientific hurdles that have impeded comparative genomic studies of cancer. This new system can reliably detect single copy deletions in chromosomes. The system includes a whole human genome microarray, reagents for sample preparation, an optimized microarray processing protocol, and software for data analysis and visualization. In this study, we determined the sensitivity, accuracy and reproducibility of the new system. Using this assay, we find that the performance of the complete system was maintained over a range of input genomic DNA from 5 ug down to 0.15 ug.

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

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.5
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• pp.569-574
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• 2011

In this paper, we introduce the subspace contrasting group identification problem and propose an algorithm to solve the problem. In order to identify contrasting groups, the algorithm first determines two groups of which attribute values are in one of the contrasting ranges specified by the analyst, and searches for the contrasting groups while increasing the dimension of subspaces with an association rule mining strategy. Because the dimension of microarray data is likely to be tens of thousands, it is burdensome to find all contrasting groups over all possible subspaces by query generation. It is very useful in the sense that the proposed method allows to find those contrasting groups without analyst's involvement.

• KOGO NEWS
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• v.5 no.4
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• pp.17-25
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• 2005

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.4
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• pp.545-549
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• 2009

Microarray data sets could contain thousands of gene expression levels and have been considered as an important source from which meaningful patterns could be extracted for further analysis in biological studies. It is sometimes necessary to retrieve out specific genes or samples of analyst's interest in an effective way. This paper is concerned with a method to make use of fuzzy signature set in order to filter out genes or samples which satisfy complicated constraints as well as simple ones. Fuzzy signatures are an extension of vector valued fuzzy sets, in which elements of the vector are allowed to have a vector. Fuzzy signature sets are similar to fuzzy signatures except that their leaf elements are fuzzy sets defined on the interval [0,1]. This paper introduces an extension of fuzzy signature sets which specifies aggregation operators at each internal node and comparison operators for aggregation. It also shows how to use the extended fuzzy signature sets in microarray data retrieval and some examples of its usage.

• Journal of KIISE:Software and Applications
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• v.30 no.12
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• pp.1124-1134
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• 2003

The development of microarray technology has supplied a large volume of data to many fields. In particular, it has been applied to prediction and diagnosis of cancer, so that it expectedly helps us to exactly predict and diagnose cancer. It is essential to efficiently analyze DNA microarray data because the amount of DNA microarray data is usually very large. Since accurate classification of cancer is very important issue for treatment of cancer, it is desirable to make a decision by combining the results of various expert classifiers rather than by depending on the result of only one classifier. Generally combining classifiers gives high performance and high confidence. In spite of many advantages of ensemble classifiers, ensemble with mutually error-correlated classifiers has a limit in the performance. In this paper, we propose the ensemble of neural network classifiers learned from negatively correlated features using three benchmark datasets to precisely classify cancer, and systematically evaluate the performances of the proposed method. Experimental results show that the ensemble classifier with negatively correlated features produces the best recognition rate on the three benchmark datasets.