• Genomics & Informatics
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• v.7 no.2
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• pp.122-130
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• 2009

A systems biology approach for the identification of perturbed molecular functions is required to understand the complex progressive disease such as breast cancer. In this study, we analyze the microarray data with Gene Ontology terms of molecular functions to select perturbed molecular functional modules in breast cancer tissues based on the definition of Gene ontology Functional Code. The Gene Ontology is three structured vocabularies describing genes and its products in terms of their associated biological processes, cellular components and molecular functions. The Gene Ontology is hierarchically classified as a directed acyclic graph. However, it is difficult to visualize Gene Ontology as a directed tree since a Gene Ontology term may have more than one parent by providing multiple paths from the root. Therefore, we applied the definition of Gene Ontology codes by defining one or more GO code(s) to each GO term to visualize the hierarchical classification of GO terms as a network. The selected molecular functions could be considered as perturbed molecular functional modules that putatively contributes to the progression of disease. We evaluated the method by analyzing microarray dataset of breast cancer tissues; i.e., normal and invasive breast cancer tissues. Based on the integration approach, we selected several interesting perturbed molecular functions that are implicated in the progression of breast cancers. Moreover, these selected molecular functions include several known breast cancer-related genes. It is concluded from this study that the present strategy is capable of selecting perturbed molecular functions that putatively play roles in the progression of diseases and provides an improved interpretability of GO terms based on the definition of Gene Ontology codes.

• KOGO NEWS
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• v.6 no.1
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• pp.29-33
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• 2006

Gene set analysis is a new concept and method. to analyze and interpret microarray gene expression data and tries to extract biological meaning from gene expression data at gene set level rather than at gene level. Compared with methods which select a few tens or hundreds of genes before gene ontology and pathway analysis, gene set analysis identifies important gene ontology terms and pathways more consistently and performs well even in gene expression data sets with minimal or moderate gene expression changes. Moreover, gene set analysis is useful for comparing multiple gene expression data sets dealing with similar biological questions. This review briefly summarizes the rationale behind the gene set analysis and introduces several algorithms and tools now available for gene set analysis.

• Journal of Information Processing Systems
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• v.3 no.1
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• pp.38-42
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• 2007

Microarray data includes tens of thousands of gene expressions simultaneously, so it can be effectively used in identifying the phenotypes of diseases. However, the retrieval of functional information from a large corpus of gene expression data is still a time-consuming task. In this paper, we propose an efficient method for identifying functional categories of differentially expressed genes from a micro-array experiment by using Gene Ontology (GO). Our method is as follows: (1) The expression data set is first filtered to include only genes with mean expression values that differ by at least 3-fold between the two groups. (2) The genes are then ranked based on the t-statistics. The 100 most highly ranked genes are selected as informative genes. (3) The t-value of each informative gene is imposed as a score on the associated GO terms. High-scoring GO terms are then listed with their associated genes and represent the functional category information of the micro-array experiment. A system called HMDA (Hallym Micro-array Data analysis) is implemented on publicly available micro-array data sets and validated. Our results were also compared with the original analysis.

• 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).

• Biomolecules & Therapeutics
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• v.30 no.1
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• pp.98-116
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• 2022

The small GTPase RhoA has been studied extensively for its role in actin dynamics. In this study, multiple bioinformatics tools were applied cooperatively to the microarray dataset GSE64714 to explore previously unidentified functions of RhoA. Comparative gene expression analysis revealed 545 differentially expressed genes in RhoA-null cells versus controls. Gene set enrichment analysis (GSEA) was conducted with three gene set collections: (1) the hallmark, (2) the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and (3) the Gene Ontology Biological Process. GSEA results showed that RhoA is related strongly to diverse pathways: cell cycle/growth, DNA repair, metabolism, keratinization, response to fungus, and vesicular transport. These functions were verified by heatmap analysis, KEGG pathway diagramming, and direct acyclic graphing. The use of multiple gene set collections restricted the leakage of information extracted. However, gene sets from individual collections are heterogenous in gene element composition, number, and the contextual meaning embraced in names. Indeed, there was a limit to deriving functions with high accuracy and reliability simply from gene set names. The comparison of multiple gene set collections showed that although the gene sets had similar names, the gene elements were extremely heterogeneous. Thus, the type of collection chosen and the analytical context influence the interpretation of GSEA results. Nonetheless, the analyses of multiple collections made it possible to derive robust and consistent function identifications. This study confirmed several well-described roles of RhoA and revealed less explored functions, suggesting future research directions.

• Molecular & Cellular Toxicology
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• v.3 no.4
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• pp.222-230
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• 2007

Some drugs may be limited in their clinical application due to their propensity towards their adverse effects. Toxicogenomic technology represents a useful approach for evaluating the toxic properties of new drug candidates early in the drug discovery process. Nitrofurantoin (NF) is clinical chemotherapeutic agent and antimicrobial and used to treatment of urinary tract infections. However, NF has been shown to result in pulmonary toxic effects. In this research, we revealed the changing expression gene profiles in BEAS-2B, human bronchial epithelial cell line, exposed to NF by using human oligonucleotide chip. Through the clustering analysis of gene expression profiles, we identified 136 up-regulated genes and 379 down-regulated genes changed by more than 2-fold by NF. This study identifies several interesting targets and functions in relation to NF-induced toxicity through a gene ontology analysis method including biological process, cellular components, molecular function and KEGG pathway.

• Genomics & Informatics
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• v.11 no.3
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• pp.135-141
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• 2013

Gene set analysis is a powerful tool for interpreting a genome-wide association study result and is gaining popularity these days. Comparison of the gene sets obtained for a variety of traits measured from a single genetic epidemiology dataset may give insights into the biological mechanisms underlying these traits. Based on the previously published single nucleotide polymorphism (SNP) genotype data on 8,842 individuals enrolled in the Korea Association Resource project, we performed a series of systematic genome-wide association analyses for 49 quantitative traits of basic epidemiological, anthropometric, or blood chemistry parameters. Each analysis result was subjected to subsequent gene set analyses based on Gene Ontology (GO) terms using gene set analysis software, GSA-SNP, identifying a set of GO terms significantly associated to each trait ($p_{corr}$ < 0.05). Pairwise comparison of the traits in terms of the semantic similarity in their GO sets revealed surprising cases where phenotypically uncorrelated traits showed high similarity in terms of biological pathways. For example, the pH level was related to 7 other traits that showed low phenotypic correlations with it. A literature survey implies that these traits may be regulated partly by common pathways that involve neuronal or nerve systems.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.4
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• pp.19-26
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• 2008

Gene set enrichment analysis (GSEA) is a computational method to identify statistically significant gene sets showing significant differences between two groups of microarray expression profiles and simultaneously uncover their biological meanings in an elegant way by employing gene annotation databases, such as Cytogenetic Band, KEGG pathways, gene ontology, and etc. For the gone set enrichment analysis, all the genes in a given dataset are first ordered by the signal-to-noise ratio between the groups and then further analyses are proceeded. Despite of its impressive results in several previous studies, however, gene ranking by the signal-to-noise ratio makes it difficult to consider highly up-regulated genes and highly down-regulated genes at the same time as the candidates of significant genes, which possibly reflect certain situations incurred in metabolic and signaling pathways. To deal with this problem, in this article, we investigate the gene set enrichment analysis method with Fisher criterion for gene ranking and also evaluate its effects in Leukemia related pathway analyses.

• 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.

• Molecular & Cellular Toxicology
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• v.1 no.4
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• pp.281-283
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• 2005

Extraction of biologically meaningful data and their validation are very important for toxicogenomics study because it deals with huge amount of heterogeneous data. BINGO is an annotation mining tool for biological interpretation of gene groups. Several statistical modeling approaches using Gene Ontology (GO) have been employed in many programs for that purpose. The statistical methodologies are useful in investigating the most significant GO attributes in a gene group, but the coherence of the resultant GO attributes over the entire group is rarely assessed. BINGO complements the statistical methods with graph-theoretic measures using the GO directed acyclic graph (DAG) structure. In addition, BINGO visualizes the consistency of a gene group more intuitively with a group-based GO subgraph. The input group can be any interesting list of genes or gene products regardless of its generation process if the group is built under a functional congruency hypothesis such as gene clusters from DNA microarray analysis.