• Journal of KIISE:Databases
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• v.30 no.6
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• pp.573-584
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• 2003

Recent massive data generation by genomics and proteomics requires bioinformatic tools to extract the biological meaning from the massive results. Here we introduce ROSPath, a database system to deal with information on reactive oxygen species (ROS)-mediated cell signaling pathways. It provides a structured repository for handling pathway related data and tools for querying, displaying, and analyzing pathways. ROSPath data model provides the extensibility for representing incomplete knowledge and the accessibility for linking the existing biochemical databases via the Internet. For flexibility and efficient retrieval, hierarchically structured data model is defined by using the object-oriented model. There are two major data types in ROSPath data model: ‘bio entity’ and ‘interaction’. Bio entity represents a single biochemical entity: a protein or protein state involved in ROS cell-signaling pathways. Interaction, characterized by a list of inputs and outputs, describes various types of relationship among bio entities. Typical interactions are protein state transitions, chemical reactions, and protein-protein interactions. A complex network can be constructed from ROSPath data model and thus provides a foundation for describing and analyzing various biochemical processes.

• Herbal Formula Science
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• v.26 no.3
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• pp.237-250
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• 2018

Objectives : This study is a pharmacological network approach, aimed to identify the potential active compounds contained in Curcumae Radix, and their associated targets, to predict the various bio-reactions involved, and finally to establish the cornerstone for the deep-depth study of the representative mechanisms. Methods : The active compounds of Curcumae Radix have been identified using Traditional Chinese Medicine System Pharmacology Database and Analysis Platform. The UniProt database was used to collect each of information of all target proteins associated with the active compounds. To find the bio-metabolic processes associated with each target, the DAVID6.8 Gene Functional classifier tool was used. Compound-Target and Target-Pathway networks were analyzed via Cytoscape 3.40. Results : The target information from 32 potential active compounds of Curcumae Radix was collected through TCMSP analysis. The active compounds interact with 133 target genes engaging in total of 885 biological pathways. The most relevant pathway was the lipid-related metabolism, in which 3 representative active compounds were naringenin, oleic acid, and ${\beta}-sitosterol$. The mostly targeted proteins in the lipid pathway were ApoB, AKT1 and PPAR. Conclusions : The pharmacological network analysis is convenient approach to predict the overall metabolic mechanisms in medicinal herb research, which can reduce the processes of various experimental trial and error and provide key clues that can be used to validate and experimentally verify the core compounds.

• The Journal of the Korea Contents Association
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• v.5 no.6
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• pp.31-40
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• 2005

In the post-genomic era, researches on proteins as well as genes have been increasingly required. Particularly, work on protein-protein interaction and protein network construction have been recently establishing. Most biologists publish their research results through papers or other media. However, biologists do not use the information effectively, because the published research results are very large. As the growth of internet field, it becomes easy to access these research results. It is important to extract information with a biological meaning from various media. Therefore, In this paper, we efficiently extract the protein information from many open papers or other media and construct the database of the extracted information. We build a protein network from the established database and then design and implement various pathway analysis algorithms which find biological meaning from the protein network.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2000.11a
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• pp.56-58
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• 2000

Post-genomics may be defined in different ways depending on how one views the challenges after the genome. A popular view is to follow the concept of the central dogma in molecular biology, namely from genome to transcriptome to proteome. Projects are going on to analyze gene expression profiles both at the mRNA and protein levels and to catalog protein 3D structure families, which will no doubt help the understanding of information in the genome. However complete, such catalogs of genes, RNAs, and proteins only tell us about the building blocks of life. They do not tell us much about the wiring (interaction) of building blocks, which is essential for uncovering systemic functional behaviors of the cell or the organism. Thus, an alternative view of post-genomics is to go up from the molecular level to the cellular level, and to understand, what I call, the "interactome"or a complete picture of molecular interactions in the cell. KEGG (http://www.genome.ad.jp/kegg/) is our attempt to computerize current knowledge on various cellular processes as a collection of "generalized"protein-protein interaction networks, to develop new graph-based algorithms for predicting such networks from the genome information, and to actually reconstruct the interactomes for all the completely sequenced genomes and some partial genomes. During the reconstruction process, it becomes readily apparent that certain pathways and molecular complexes are present or absent in each organism, indicating modular structures of the interactome. In addition, the reconstruction uncovers missing components in an otherwise complete pathway or complex, which may result from misannotation of the genome or misrepresentation of the KEGG pathway. When combined with additional experimental data on protein-protein interactions, such as by yeast two-hybrid systems, the reconstruction possibly uncovers unknown partners for a particular pathway or complex. Thus, the reconstruction is tightly coupled with the annotation of individual genes, which is maintained in the GENES database in KEGG. We are also trying to expand our literature surrey to include in the GENES database most up-to-date information about gene functions.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.2
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• pp.290-296
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• 2019

Objective: Pigs share many physiological, anatomical and genomic similarities with humans, which make them suitable models for biomedical researches. Understanding the genetic status of Yucatan miniature pigs (YMPs) and their association with human diseases will help to assess their potential as biomedical model animals. This study was performed to identify non-synonymous single nucleotide polymorphisms (nsSNPs) in selective sweep regions of the genome of YMPs and present the genetic nsSNP distributions that are potentially associated with disease occurrence in humans. Methods: nsSNPs in whole genome resequencing data from 12 YMPs were identified and annotated to predict their possible effects on protein function. Sorting intolerant from tolerant (SIFT) and polymorphism phenotyping v2 analyses were used, and gene ontology (GO) network and Kyoto encyclopedia of genes and genomes (KEGG) pathway analyses were performed. Results: The results showed that 8,462 genes, encompassing 72,067 nsSNPs were identified, and 118 nsSNPs in 46 genes were predicted as deleterious. GO network analysis classified 13 genes into 5 GO terms (p<0.05) that were associated with kidney development and metabolic processes. Seven genes encompassing nsSNPs were classified into the term associated with Alzheimer's disease by referencing the genetic association database. The KEGG pathway analysis identified only one significantly enriched pathway (p<0.05), hsa04080: Neuroactive ligand-receptor interaction, among the transcripts. Conclusion: The number of deleterious nsSNPs in YMPs was identified and then these variants-containing genes in YMPs data were adopted as the putative human diseases-related genes. The results revealed that many genes encompassing nsSNPs in YMPs were related to the various human genes which are potentially associated with kidney development and metabolic processes as well as human disease occurrence.

• Journal of Digital Contents Society
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• v.19 no.9
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• pp.1795-1801
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• 2018

Genome-Wide Association Study (GWAS) have been used to identify susceptibility genes for complex human diseases and many recent studies succeed to report common genetic factors for various diseases. Unfortunately, it is hard to understand all biological functions and mechanisms around the complex disease with GWAS only although the number of known associated genes with diseases is increased drastically because GWAS is a single locus based approach while not a gene but numerous factors may affect a disease associated pathways. PRaDA generates a combined report with genes, pathways and Gene Ontology (GO) using single nucleotide polymorphism (SNP) analysis output. The PRaDA reports not only directly associated pathways but also functionally related ones for identifying accumulated effects of low p-value SNPs. Through integrated information including indirect functional effects, user could have insights of overall disease mechanisms and markers.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.9
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• pp.5281-5286
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• 2013

Purpose: Prostate cancer caused by the abnormal disorderly growth of prostatic acinar cells is the most prevalent cancer of men in western countries. We aimed to screen out differentially expressed genes (DEGs) and explore small molecule drugs for prostate cancer. Materials and Methods: The GSE3824 gene expression profile of prostate cancer was downloaded from Gene Expression Omnibus database which including 21 normal samples and 18 prostate cancer cells. The DEGs were identified by Limma package in R language and gene ontology and pathway enrichment analyses were performed. In addition, potential regulatory microRNAs and the target sites of the transcription factors were screened out based on the molecular signature database. In addition, the DEGs were mapped to the connectivity map database to identify potential small molecule drugs. Results: A total of 6,588 genes were filtered as DEGs between normal and prostate cancer samples. Examples such as ITGB6, ITGB3, ITGAV and ITGA2 may induce prostate cancer through actions on the focal adhesion pathway. Furthermore, the transcription factor, SP1, and its target genes ARHGAP26 and USF1 were identified. The most significant microRNA, MIR-506, was screened and found to regulate genes including ITGB1 and ITGB3. Additionally, small molecules MS-275, 8-azaguanine and pyrvinium were discovered to have the potential to repair the disordered metabolic pathways, abd furthermore to remedy prostate cancer. Conclusions: The results of our analysis bear on the mechanism of prostate cancer and allow screening for small molecular drugs for this cancer. The findings have the potential for future use in the clinic for treatment of prostate cancer.

• Genomics & Informatics
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• v.7 no.1
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• pp.38-40
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• 2009

The EST Knowledge Integrated System, EKIS (http://ekis.kribb.re.kr), was established as a part of Korea's Ministry of Education, Science and Technology initiative for genome sequencing and application research of the biological model organisms (GEAR) project. The goals of the EKIS are to collect EST information from GEAR projects and make an integrated database to provide transcriptomic and metabolomic information for biological scientists. The EKIS constitutes five independent categories and several retrieval systems in each category for incorporating massive EST data from high-throughput sequencing of 65 different species. Through the EKIS database, scientists can freely access information including BLAST functional annotation as well as Genechip and pathway information for KEGG. By integrating complex data into a framework of existing EST knowledge information, the EKIS provides new insights into specialized metabolic pathway information for an applied industrial material.

• Herbal Formula Science
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• v.31 no.4
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• pp.295-313
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• 2023

Objectives : While treatments for cancer are advancing, the development of effective treatments for cancer metastasis, the main cause of cancer patient death, remains insufficient. Recent studies on Dichroae Radix have revealed that its active ingredients have the potential to inhibit cancer metastasis. This study aimed to investigate the cancer metastasis inhibitory effect of Dichroae Radix using network pharmacological analysis. Methods : The active compounds of Dichroae Radix have been identified using Traditional Chinese Medicine System Pharmacology Database and Analysis Platform. The UniProt database was used to collect each of information of all target proteins associated with the active compounds. To find the bio-metabolic processes associated with each target, the DAVID6.8 Gene Functional classifier tool was used. Compound-Target and Target-Pathway networks were analyzed via Cytoscape 3.40. Results : In total, 25 active compounds and their 62 non-redundant targets were selected through the TCMSP database and analysis platform. The target genes underwent gene ontology and pathway enrichment analysis. The gene list applied to the gene ontology analysis revealed associations with various biological processes, including signal transduction, chemical synaptic transmission, G-protein-coupled receptor signaling pathways, response to xenobiotic stimulus, and response to drugs, among others. A total of eleven genes, including HSP90AB1, CALM1, F2, AR, PAKACA, PTGS2, NOS2, RXRA, ESR1, ESR2, and NCOA1, were found to be associated with biological pathways related to cancer metastasis. Furthermore, nineteen of the active compounds from Dichroae Radix were confirmed to interact with these genes. Conclusions : The results provide valuable insights into the mechanism of action and molecular targets of Dichroae Radix. Notably, Berberine, the main active ingredient of Dichroae Radix, plays a significant role in degrading AR proteins in advanced prostate cancer. Further studies and validations can provide crucial data to advance cancer metastasis prevention and treatment strategies.

• Genomics & Informatics
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• v.10 no.1
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• pp.23-32
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• 2012

There are lots of studies attempting to identify the expression changes in oral squamous cell carcinoma. Most studies include insufficient samples to apply statistical methods for detecting significant gene sets. This study combined two small microarray datasets from a public database and identified significant genes associated with the progress of oral squamous cell carcinoma. There were different expression scales between the two datasets, even though these datasets were generated under the same platforms - Affymetrix U133A gene chips. We discretized gene expressions of the two datasets by adjusting the differences between the datasets for detecting the more reliable information. From the combination of the two datasets, we detected 51 significant genes that were upregulated in oral squamous cell carcinoma. Most of them were published in previous studies as cancer-related genes. From these selected genes, significant genetic pathways associated with expression changes were identified. By combining several datasets from the public database, sufficient samples can be obtained for detecting reliable information. Most of the selected genes were known as cancer-related genes, including oral squamous cell carcinoma. Several unknown genes can be biologically evaluated in further studies.