• Proceedings of the Korean Society for Bioinformatics Conference
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• 2000.11a
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• pp.74-82
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• 2000

Prostate cancer initially responds and regresses in response to androgen depletion therapy, but most human prostate cancers will eventually recur, and re-grow as an androgen independent tumor. Once these tumors become hormone refractory, they usually are incurable leading to death for the patient. Little is known about the molecular details of how prostate cancer cells regress following androgen ablation and which genes are involved in the androgen independent growth following the development of resistance to therapy. Such knowledge would reveal putative drug targets useful in the rational therapeutic design to prevent therapy resistance and control androgen independent growth. The application of genome scale technologies have permitted new insights into the molecular mechanisms associated with these processes. Specifically, we have applied functional genomics using high density cDNA microarray analysis for parallel gene expression analysis of prostate cancer in an experimental xenograft system during androgen withdrawal therapy, and following therapy resistance, The large amount of expression data generated posed a formidable bioinformatics challenge. A novel template based gene clustering algorithm was developed and applied to the data to discover the genes that respond to androgen ablation. The data show restoration of expression of androgen dependent genes in the recurrent tumors and other signaling genes. Together, the discovered genes appear to be involved in prostate cancer cell growth and therapy resistance in this system. We have also developed and applied tissue microarray (TMA) technology for high throughput molecular analysis of hundreds to thousands of clinical specimens simultaneously. TMA analysis was used for rapid clinical translation of candidate genes discovered by cDNA microarray analysis to determine their clinical utility as diagnostic, prognostic, and therapeutic targets. Finally, we have developed a bioinformatic approach to combine pharmacogenomic data on the efficacy and specificity of various drugs to target the discovered prostate cancer growth associated candidate genes in an attempt to improve current therapeutics.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.786-792
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• 2012

A microarray is a collection of thousands of DNAs or RNAs arranged on a substrate, and it enables one to navigate large amounts of gene expression. However, a researcher uses his designed experimental methods to focus on particular phenotypes from the available mass of data. In this paper, we used MicroRNAs(miRNAs) and Protein-Protein Interation(PPI) databases to enhance and expand meanings in microarray data. Further, the expanded data are linked with the Online Mendelian Inheritance in Man(OMIM), and International Statistical Classification of Diseases and Related Health Problems, $10^{th}$ Revision(ICD-10), in order to extract common genetic relationships between diseases. This approach, we expect, should provide new biological views.

• Genomics & Informatics
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• v.4 no.3
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• pp.129-132
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• 2006

Toxicogenomics has recently emerged in the field of toxicology and the DNA microarray technique has become common strategy for predictive toxicology which studies molecular mechanism caused by exposure of chemical or environmental stress. Although microarray experiment offers extensive genomic information to the researchers, yet high dimensional characteristic of the data often makes it hard to extract meaningful result. Therefore we developed toxicant enrichment analysis similar to the common enrichment approach. We also developed web-based system graPT to enable considerable prediction of toxic endpoints of experimental chemical.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2001.10a
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• pp.11-28
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• 2001

The Korean functional Genomics Project focuses on stomach and liver cancers. Specimens collected by six hospital teams are used in BNA microarray experiments. Experimental conditions, spot measurement data, and the associated clinical information are stored in a relational database. Microarray database schema was developed based on EBI's ArrayExpress. A diagrammatic representation of the schema is used to help navigate over marty tables in the database. Field description, table-to-table relationship, and other database features are also stored in the database and these are used by a PERL interface program to generate web-based input forms on the fly. As such, it is rather simple to modify the database definition and implement controlled vocabularies. This PERL program is a general-purpose utility which can be used for inputting and updating data in relational databases. It supports file upload and user-supplied filters of uploaded data. Joining related tables is implemented using JavaScripts, allowing this step to be deferred to a later stage. This feature alleviates the pain of inputting data into a multi-table database and promotes collaborative data input among several teams. Pathological finding, clinical laboratory parameters, demographical information, and environmental factors are also collected and stored in a separate database. The same PERL program facilitated developing this database and its user-interface.

• The Korea Genome Conference
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• 2003.09a
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• pp.53.2-53.2
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• 2003

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.9
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• pp.2032-2037
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• 2011

Marker 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 marker genes that are selected by ranking genes according to statistics 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 mult-perceptron neural network layer. The Result that apply Multi-Layer perceptron algorithm at Microarray data set including eight of marker gene that are selected using ANOVA method after Lowess normalization represent the highest classification accuracy of 99.32% and the lowest prediction error estimate.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.471-475
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• 2008

Microarray technology in biological science enables molecular level observations and analyses on the biological phenomina by allowing to measure the RNA expression profiles in cells. Microarray data analysis is applied in various purposes such as identifying significant genes which react to drug treatment, understanding the genome scale phenomina. In drug response experiments, the microarray-based gene expression analysis could provide meaningful information. It is sometimes needed to identify the genes which shows different expression behavior for treatment group and normal group each other. When the normal group shows the medium level expression, it is not easy to discriminate the group just by expression level comparison. This paper proposes a method which selects group-wise representative values for each gene and sets the value range of the groups in order to filter out the genes with specific pattern. It also shows some experiment results.

• Biotechnology and Bioprocess Engineering:BBE
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• v.10 no.5
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• pp.432-443
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• 2005

Microarray technology has contributed Significantly to the understanding of bacterial genetics and transcriptional regulation. One neglected aspect of this technology has been optimization of microarray-generated signals and quality of generated information. Full genome microarrays were developed for Clostridium acetobutylicum through spotting of PCR products that were designed with minimal homology with all other genes within the genome. Using statistical analyses it is demonstrated that Signal quality is significantly improved by increasing the hybridization volume. possibly increasing the effective number of transcripts available to bind to a given spot, while changes in labeled probe amounts were found to be less sensitive to improving signal quality. In addition to Q-RT-PCR, array validation was tested by examining the transcriptional program of a mutant (M5) strain lacking the pSOL1 178-gene megaplasmid relative to the wildtype (WT) strain. Under optimal conditions, it is demonstrated that the fraction of false positive genes is 1% when considering differentially expressed genes and 7% when considering all genes with signal above background. To enhance genomic-scale understanding of organismal physiology, using data from these microarrays we estimated that $40{\sim}55%$ of the C. acetobutylicum genome is expressed at any time during batch culture, similar to estimates made for Bacillus subtilis.

• BMB Reports
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• v.41 no.9
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• pp.670-677
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• 2008

A cDNA microarray composed of 2,028 different ESTs from two shrimp species, Penaeus monodon and Masupenaeus japonicus, was employed to identify yellow head virus (YHV)-responsive genes in hemocytes of P. monodon. A total of 105 differentially expressed genes were identified and grouped into five different clusters according to their expression patterns. One of these clusters, which comprised five genes including cathepsin L-like cysteine peptidase, hypothetical proteins and unknown genes, was of particular interest because the transcripts increased rapidly ($\leq$ 0.25 hours) and reached high expression levels in response to YHV injection. Microarray data were validated by realtime RT-PCR analyses of selected differentially expressed transcripts. In addition, comparative analysis of the hemocyte transcription levels of three of these genes between surviving and non-surviving shrimp revealed significantly higher expression levels in surviving shrimp.

• The Korean Journal of Physiology and Pharmacology
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• v.7 no.5
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• pp.279-282
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• 2003

To investigate the effect of fluoxetine, one of selective serotonin reuptake inhibitors (SSRIs), on the immune system, human peripheral blood mononuclear cells (PBMC) were treated with fluoxetine $(10^{-7}\;M)$ for 24 h, and immune-related genes were analyzed by cDNA microarray. Expression of the immunerelated genes such as CD107b (LAMP-2), CD47 receptor (thrombospondin receptor), CD5 antigen-like (scavenger receptor cysteine rich family), copine III (CPNE3), interleukin (IL)-18 (interferon-gammainducing factor), integrin alpha 4 (CD49d), integrin alpha L subunit (CD11a), IL-3 receptor alpha subunit, L apoferritin, and small inducible cytokine subfamily A (Cys-Cys) member 13 (SCYA13) was induced by fluoxetine. This result suggests that fluoxetine may affect the immune system, and provides fundamental data for the involvement of SSRIs on immunoregulation.