• Molecules and Cells
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• v.46 no.1
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• pp.65-67
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• 2023

SMILES (simplified molecular-input line-entry system) information of small molecules parsed by one-hot array is passed to a convolutional neural network called black box. Outputs data representing a gene signature is then matched to the genetic signature of a disease to predict the appropriate small molecule. Efficacy of the predicted small molecules is examined by in vivo animal models. GSEA, gene set enrichment analysis.

• Korean Journal of Biological Psychiatry
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• v.8 no.2
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• pp.203-207
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• 2001

The genome sequencing project has generated and will continue to generate enormous amounts of sequence data including 5 eukaryotic and about 60 prokaryotic genomes. Given this ever-increasing amounts of sequence information, new strategies are necessary to efficiently pursue the next phase of the genome project-the elucidation of gene expression patterns and gene product function on a whole genome scale. In order to assign functional information to the genome sequence, DNA chip(or gene microarray) technology was developed to efficiently identify the differential expression pattern of independent biological samples. DNA chip provides a new tool for genome expression analysis that may revolutionize many aspects of biotechnology including new drug discovery and disease diagnostics.

• Genomics & Informatics
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• v.13 no.3
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• pp.76-80
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• 2015

Type 2 diabetes mellitus is a complex metabolic disorder associated with multiple genetic, developmental and environmental factors. The recent advances in gene expression microarray technologies as well as network-based analysis methodologies provide groundbreaking opportunities to study type 2 diabetes mellitus. In the present study, we used previously published gene expression microarray datasets of human skeletal muscle samples collected from 20 insulin sensitive individuals before and after insulin treatment in order to construct insulin-mediated regulatory network. Based on a motif discovery method implemented by iRegulon, a Cytoscape app, we identified 25 candidate regulons, motifs of which were enriched among the promoters of 478 up-regulated genes and 82 down-regulated genes. We then looked for a hierarchical network of the candidate regulators, in such a way that the conditional combination of their expression changes may explain those of their target genes. Using Genomica, a software tool for regulatory network construction, we obtained a hierarchical network of eight regulons that were used to map insulin downstream signaling network. Taken together, the results illustrate the benefits of combining completely different methods such as motif-based regulatory factor discovery and expression level-based construction of regulatory network of their target genes in understanding insulin induced biological processes and signaling pathways.

• Bioinformatics and Biosystems
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• v.2 no.2
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• pp.52-57
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• 2007

The computational discovery of transcription factor binding site is one of the important tools in the genetic and genomic analysis. Rough prediction of gene regulation network and finding possible co-regulated genes are typical applications of the technique. Countless motif-discovery algorithms have been proposed for the past years. However, there is no dominant algorithm yet. Each algorithm does not give enough accuracy without extensive information. In this paper, we explore the possibility of combining multiple algorithms for the one integrated result in order to improve the performance and the convenience of researchers. Moreover, we apply new high order information that is reorganized from the set of basis predictions to the final prediction.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.258-263
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• 2005

Haplotype-based analysis using high-density SNP markers have gained a great attention in evaluating genes in gene analysis and various clinical situations. However, there has been no research on disease diagnostic modeling based on SNPs analysis to our knowledge. The purpose of this study is to explore how knowledge discovery techniques are applied in medical informatics area and proposes a Case Based Reasoning (CBR) technique for diagnosis of gastric caner using Single Nucleotide Polymorphism(SNP).

• Proceedings of the Korean Society of Toxicology Conference
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• 2002.11b
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• pp.116-116
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• 2002

It is believed that cell and/or tissue toxicity is resulted from alterations in expression of many genes in response to environmental stresses or toxicants. New technology, such as DNA microarray analysis, can measure the expression of thousands of genes at a time providing the potential to accelerate discovery of toxicant pathways and specific gene targets.(omitted)

• The Korean Journal of Applied Statistics
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• v.25 no.2
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• pp.269-277
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• 2012

In microarray data analysis, recent efforts have focused on the discovery of gene sets from a pathway or functional categories such as Gene Ontology terms(GO terms) rather than on individual gene function for its direct interpretation of genome-wide expression data. We introduce a meta-analysis method that combines $p$-values for changes of each gene in the group. The method measures the significance of overall treatment-induced change in a gene group. An application of the method to a real data demonstrates that it has benefits over other statistical methods such as Fisher's exact test and permutation methods. The method is implemented in a SAS program and it is available on the author's homepage(http://cafe.daum.net/go.analysis).

• Journal of the Korean Data and Information Science Society
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• v.25 no.5
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• pp.971-986
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• 2014

When thousands of hypotheses are tested simultaneously, the probability of rejecting any true hypotheses increases, and large multiplicity problems are generated. To solve these problems, researchers have proposed different approaches to multiple testing methods, considering family-wise error rate (FWER), false discovery rate (FDR) or false nondiscovery rate (FNR) as a type I error and some test statistics. In this article, we discuss Bonferroni (1960), Holm (1979), Benjamini and Hochberg (1995) and Benjamini and Yekutieli (2001) procedures based on T statistics, modified T statistics or local-pooled-error (LPE) statistics. We also consider Sun and Cai (2007) procedure based on Z statistics. These procedures are compared in the simulation and applied to Arabidopsis microarray gene expression data to identify differentially expressed genes.

• Genomics & Informatics
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• v.6 no.1
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• pp.14-17
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• 2008

GENDISCAN study (Gene Discovery for Complex traits in Asian population of Northeast area) was designed to incorporate methodologies which enhance the power to identify genetic variations underlying complex disorders. Use of population isolates as the target population is a unique feather of this study. However, population isolates may have hidden inbreeding structures which can affect the validity of the study. To understand how this issue may affect results of GENDISCAN, we estimated inbreeding coefficients in two study populations in Mongolia. We analyzed the status of Hardy-Weinberg Equilibrium (HWE), polymorphism information contents (PIC), heterozygosity, allelic diversity, and inbreeding coefficients, using 317 and 1,044 STR (short tandem repeat) markers in Orkhontuul and Dashbalbar populations. HWE assumptions were generally met in most markers (88.6% and 94.2% respectively), and single marker PIC ranged between 0.2 and 0.9. Inbreeding coefficients were estimated to be 0.0023 and 0.0021, which are small enough to assure that conventional genetic analysis would work without any specific modification. We concluded that the population isolates used in GENDISCAN study would not present significant inflation of type I errors from inbreeding effects in its gene discovery analysis.

• Research in Plant Disease
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• v.25 no.4
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• pp.205-212
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• 2019

Analysis of differentially expressed genes has assisted discovery of gene sets involved in particular biological processes. The purpose of this study was to identify genes involved in appressorium formation in the rice blast fungus Magnaporthe oryzae via analysis of cDNA-amplified fragment length polymorphisms. Amplification of appressorial and vegetative mycelial cDNAs using 28 primer combinations generated over 200 differentially expressed transcript-derived fragments (TDFs). TDFs were excised from gels, re-amplified by PCR, cloned, and sequenced. Forty-four of 52 clones analyzed corresponded to 42 genes. Quantitative real-time PCR showed that expression of 23 genes was up-regulated during appressorium formation, one of which was the MCK1 gene that had been shown to be involved in appressorium formation. This study will be providing valuable resources for identifying the genes such as pathogenicity-related genes in M. oryzae.