• 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.

• Journal of KIISE:Computing Practices and Letters
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• v.12 no.3
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• pp.183-191
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• 2006

As genetic research is getting more active, data construction of genes are needed in the field of biology. Therefore, Gene Ontology Consortium has constructed genetic information by OWL, which is Ontology description language published by W3C. However, previous browsers for Gene Ontology only support simple searching mechanisms based on keyword, tree, and graph, but it is not able to search high quality information considering various relationships. In this paper, we suggest browsing technique which integratesvarious searching methods to support researchers who are doing actually experiment in biology field. Also, instead of typing a query, we propose querv generation technique which constructs query while browsing and query translation technique which translate generated query into SeRQL query It is convenient for user and enables user to obtain high quality information. And by this GO Guide browser, it has been shown that the information of Gene Ontology could be used efficiently.

• Journal of KIISE:Databases
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• v.33 no.7
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• pp.682-692
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• 2006

Searching for gene products which have similar biological functions are crucial for bioinformatics. Modern day biological databases provide the functional description of gene products using Gene Ontology(GO). In this paper, we propose a technique for semantic similarity search for gene products using the GO annotation information. For this purpose, an information-theoretic measure for semantic similarity between gene products is defined. And an algorithm for semantic similarity search using this measure is proposed. We adapt Fagin's Threshold Algorithm to process the semantic similarity query as follows. First, we redefine the threshold for our measure. This is because our similarity function is not monotonic. Then cluster-skipping and the access ordering of the inverted index lists are proposed to reduce the number of disk accesses. Experiments with real GO and annotation data show that GORank is efficient and scalable.

• 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.

• Journal of KIISE:Computing Practices and Letters
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• v.12 no.4
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• pp.219-228
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• 2006

Many biomedical research groups have been trying to share their outputs to increase the efficiency of research. As part of their efforts, a common ontology named Gene Ontology(GO), which comprises controlled vocabulary for the functions of genes, was built. However, data from many research groups are distributed and most systems don't support integrated semantic queries on them. Furthermore, the semantics of the associations between concepts from external classification systems and GO are still not clarified, which makes integrated semantic query infeasible. In this paper we present an ontology matching and integration system, called AutoGOA, which first resolves the semantics of the associations between concepts semi-automatically, and then constructs integrated ontology containing concepts from GO and external classification systems. Also we describe a web-based application, named GOGuide II, which allows the user to browse, query and visualize integrated data.

• Genomics & Informatics
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• v.16 no.1
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• pp.14-20
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• 2018

Despite the importance of mutation rate, some difficulties exist in estimating it. Next-generation sequencing (NGS) data yields large numbers of single-nucleotide polymorphisms, which can make it feasible to estimate substitution rates. The genetic substitution rates of Hanwoo and Holstein cattle were estimated using NGS data. Our main findings was to calculate the gene's substitution rates. Through estimation of genetic substitution rates, we found: diving region of altered substitution density exists. This region may indicate a boundary between protected and unprotected genes. The protected region is mainly associated with the gene ontology terms of regulatory genes. The genes that distinguish Hanwoo from Holstein in terms of substitution rate predominantly have gene ontology terms related to blood and circulatory system. This might imply that Hanwoo and Holstein evolved with dissimilar mutation rates and processes after domestication. The difference in meat quality between Hanwoo and Holstein could originate from differential evolution of the genes related to these blood and circulatory system ontology terms.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06a
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• pp.88-90
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• 2006

본 논문에서는 SBML문서를 효율적으로 저장관리 할 수 있는 Gene Ontology 기반의 SBML 문서관리 시스템을 제안한다. SBML은 시스템생물학에서 생화학적 네트워크 데이터의 교환 표준으로 연구 개발되었으며, 다수의 생화학적 네트워크 데이터베이스들이 SBML을 이용하여 데이터를 제공해주고 있다. 이러한 SBML 문서를 통해 서로 다른 데이터베이스 또는 응용 프로그램간 정보를 교환으로 사용되고 있으며, 그 양 또한 급속하게 증가하고 있다. 따라서 본 논문에서는 이러한 대량의 SBML 문서를 효율적으로 저장, 검색 할 수 있는 문서관리시스템을 제안한다. 제안된 시스템은 OODB를 사용하여 효율적으로 SBML 문서를 저장관리하며, Gene Ontology를 기반으로 생화학적 용어의 모호성을 해결하고, SBML문서간의 발생하는 데이터 중복을 제거하여 데이터의 품질을 제고하였다.

• 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.

• Genomics & Informatics
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• v.13 no.1
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• pp.7-14
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• 2015

During recent years, there has been exponential growth in biological information. With the emergence of large datasets in biology, life scientists are encountering bottlenecks in handling the biological data. This study presents an integrated geographic information system (GIS)-ontology application for handling microbial genome data. The application uses a linear referencing technique as one of the GIS functionalities to represent genes as linear events on the genome layer, where users can define/change the attributes of genes in an event table and interactively see the gene events on a genome layer. Our application adopted ontology to portray and store genomic data in a semantic framework, which facilitates data-sharing among biology domains, applications, and experts. The application was developed in two steps. In the first step, the genome annotated data were prepared and stored in a MySQL database. The second step involved the connection of the database to both ArcGIS and $Prot{\acute{e}}g{\acute{e}}$ as the GIS engine and ontology platform, respectively. We have designed this application specifically to manage the genome-annotated data of rumen microbial populations. Such a GIS-ontology application offers powerful capabilities for visualizing, managing, reusing, sharing, and querying genome-related data.