• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2001.07a
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• pp.757-760
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• 2001

We have used the random fluidic self-assembly (RFSA) technique based on the chip pattern of hydrophobic self-assembly layers to assemble microfabricated particles onto the chip pattern. Immobilization of DNA, fabrication of the particles and the chip pattern, arrangement of the particles on the chip pattern, and recognition of each using DNA fluorescence measurement were carried out. Establishing the walls, the arrangement stability of the particles was improved. Each DNA is able to distinguish by using the lithography process on the particles. Advantages of this method are process simplicity, wide applicability and stability. It is thought that this method can be applicable as a new fabrication technology to develop a minute integration type biosensor microarray.

• The Korean Journal of Applied Statistics
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• v.18 no.2
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• pp.371-379
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• 2005

In this paper, we propose a pattern consistency index for detecting heterogeneous time series that deviate from the representative pattern of each cluster in clustering time course gene expression data using the Pearson correlation coefficient. We examine its usefulness by applying this index to serum time course gene expression data from microarrays.

• Communications for Statistical Applications and Methods
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• v.11 no.2
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• pp.247-254
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• 2004

Tusher, Tibshirani and Chu (2001) suggested SAM (Significance Analysis of Microarrays) to compare two groups under different conditions for each gene, using microarray data. They used two sample t-statistic adding fudge factor in the denominator to prevent the value of statistic from being inflated by large sample variance, which might result in significant difference despite of a small value in the numerator. This paper aims at finding robust fudge factor and replacing it in two-sample t-statistic used in SAM, which we call Modified SAM (MSAM). Using the simulated data and data used in Dudoit et al.(2002), it is shown that MSAM find significant genes better and has less error rate than SAM.

• Genomics & Informatics
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• v.1 no.1
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• pp.20-24
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• 2003

The analysis of DNA microarry data is one of the most important things for functional genomics research. The matrix representation of microarray data and its successive 'optimal' incisional hyperplanes is a useful platform for developing optimization algorithms to determine the optimal partitioning of pairwise proximity matrix representing completely connected and weighted graph. We developed Deterministic Annealing (DA) approach to determine the successive optimal binary partitioning. DA algorithm demonstrated good performance with the ability to find the 'globally optimal' binary partitions. In addition, the objects that have not been clustered at small non­zero temperature, are considered to be very sensitive to even small randomness, and can be used to estimate the reliability of the clustering.

• Proceedings of the Korean Society of Toxicology Conference
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• 2003.05a
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• pp.36-37
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• 2003

The potential application of toxicogenomics to predictive toxicology has been discussed widely, but the utility of the approach remains largely unproven. Using cDNA microarrays, we have compared the gene expression profiles produced in mouse lymphoma cells by three genotoxic compounds, hydroxyurea (a carcino- gen), p-anisidine (a noncarcinogen) and paclitaxel (carcinogenicity unknown). (omitted)

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.4
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• pp.1691-1699
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• 2016

Ovarian cancer is possibly the sixth most common malignancy worldwide, in Mexico representing the fourth leading cause of gynecological cancer death more than 70% being diagnosed at an advanced stage and the survival being very poor. Ovarian tumors are classified according to histological characteristics, epithelial ovarian cancer as the most common (~80%). We here used high-density microarrays and a systems biology approach to identify tissue-associated deregulated genes. Non-malignant ovarian tumors showed a gene expression profile associated with immune mediated inflammatory responses (28 genes), whereas malignant tumors had a gene expression profile related to cell cycle regulation (1,329 genes) and ovarian cell lines to cell cycling and metabolism (1,664 genes).

• Proceedings of the Microbiological Society of Korea Conference
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• 2002.10a
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• pp.3-14
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• 2002

Functional genomics aims at uncovering useful information carried on genome sequences and at using it to understand the mechanisms of biological function. Elucidating the unknown biological functions of new genes based upon the genomics rationales will greatly speed up the extensive understanding of biocatalysis and biodegradation in biological world including microorganisms. DNA microarrays generate a system for the simultaneous measurement of the expression level of thousands of genes in a single hybridization assay. Their data mining (transcriptome) strategy has two categories: differential gene expression and coordinated gene expression. Furthermore, measurement of proteins (proteome) generates information on how the transcribed sequences end up as functional characteristics within the cell, and quantitation of metabolites yields information on how the functional proteins act to produce energy and process substrates (metabolome). Various composite functional genomics databases containing genetic, enzymatic and metabolic information have been developed and will contribute to the understanding of the life blue print and the new discoveries and practices in biocatalysis and biodegradation that could enrich their industrial and environmental applications.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06a
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• pp.82-84
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• 2006

최근의 생물학적인 연구에 DNA microarray가 널리 쓰이고 있기 때문에, 이러한 DNA microarray를 구성하는데 필요한 probe design 작업의 중요성이 점차 커져가고 있다. 이 논문에서는 probe design 문제를 thermodynamic fitness function이 2개인 multi-objective optimization 작업으로 변환한 뒤, ${\epsilon}$-multiobjective evolutionary algorithm을 이용하여 probe set을 찾는다. 또한, probe 탐색공간의 크기를 줄이기 위하여 각 DNA sequence의 primer 영역을 찾는 작업을 진행하며, 사용자가 직접 프로그램을 테스트할 수 있는 웹사이트를 제공한다. 실험 대상으로는 mycoides를 선택하였으며, 이 논문에서 제안된 방법을 사용하여 성공적으로 probe set을 발견할 수 있었다.

• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.475-486
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• 2015

Mining the microarray data accumulated in the public data repositories can save experimental cost and time and provide valuable biomedical information. Big data analysis pooling multiple data sets increases statistical power, improves the reliability of the results, and reduces the specific bias of the individual study. However, integrating several data sets from different studies is needed to deal with many problems. In this study, I limited the focus to the cross platform classification that the platform of a testing sample is different from the platform of a training set, and suggested a simple classification method based on rank. This method is compared with the diagonal linear discriminant analysis, k nearest neighbor method and support vector machine using the cross platform real example data sets of two cancers.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2815-2817
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• 2002

Classification method of cancers using cDNA microarrays data was developed using genetic algorithms and neural networks. For gene selection, 2308 genes were ranked using genetic algorithms, and selected by frequency number of selection from 1000 of genetic iterative runs. To calculate fitness values, artificial neural networks are used as classifier. The small, round blue cell tumors (SRBCTs) which is difficult to distinguish via pathological single test was used as test diseases for classification, and the test results showed the 96% of exact classification capability for 25 test samples.