• Journal of the Computational Structural Engineering Institute of Korea
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• v.21 no.1
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• pp.113-124
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• 2008

Distributed operation of overall structural design process, by which product optimization and process parallelization are simultaneously implemented, is presented in this paper. The database-interacted hybrid method, which selectively takes the accustomed procedure of the conventional method in the framework of the optimal design, is utilized here. The staged application of design constraints reduces the computational burden for large complex optimization problems. Two kinds of numeric and graphic processes are simultaneously implemented by concurrent engineering approach in the distributed environment of PC networks. The former is based on finite element optimization method and the latter is represented by AutoCAD using AutoLISP programming language. Numerical computation and database interaction on servers and graphic works on independent clients are communicated through message passing. The numerical experiments for some steel truss models show the validity and usability of the method. This study has sufficient adaptability and expandability, in that it is based on general methodologies and industry standard platforms.

• Journal of Life Science
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• v.14 no.6 s.67
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• pp.997-1002
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• 2004

This paper deals with the WASPIFA (Web-based Assistant System for Protein-protein Interaction and Function Analysis) system that can provide the comprehensive information on Protein-protein interaction and function concerned with function analysis. Different from existing systems for protein function and protein-protein interaction analysis, which provide fragmentary information restricted to specific field, our system furnishes end-user with comprehensive and synthetic information on the input sequence to be analyzed, including function and annotation information, domain information, and interaction relationship information. The synthetic information that our system contains as local databases has been extracted from many resources related to function, annotation, motif and domain by various pre-processing. Employing our system, end-users can evaluate and judge the synthetic results to do protein interaction and function analysis effectively. In addition, the WASPIFA system is equipped with automatic system management and data update function that facilitates system manager to maintain and manage it efficiently.

• Journal of KIISE:Computing Practices and Letters
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• v.10 no.4
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• pp.299-308
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• 2004

In this paper, we propose a probabilistic framework to predict the interaction probability of proteins. The notion of domain combination and domain combination pair is newly introduced and the prediction model in the framework takes domain combination pair as a basic unit of protein interactions to overcome the limitations of the conventional domain pair based prediction systems. The framework largely consists of prediction preparation and service stages. In the prediction preparation stage, two appearance probability matrices, which hold information on appearance frequencies of domain combination pairs in the interacting and non-interacting sets of protein pairs, are constructed. Based on the appearance probability matrix, a probability equation is devised. The equation maps a protein pair to a real number in the range of 0 to 1. Two distributions of interacting and non-interacting set of protein pairs are obtained using the equation. In the prediction service stage, the interaction probability of a Protein pair is predicted using the distributions and the equation. The validity of the prediction model is evaluated for the interacting set of protein pairs in Yeast organism and artificially generated non-interacting set of protein pairs. When 80% of the set of interacting protein pairs in DIP database are used as teaming set of interacting protein pairs, very high sensitivity(86%) and specificity(56%) are achieved within our framework.

• 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.

• Proceedings of the Korea Contents Association Conference
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• 2004.11a
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• pp.511-515
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• 2004

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, since the published research results are very large. As the growth of internet, it becomes easy to access very large research results. It is significantly important to extract information with a biological meaning from varisous media. Therefore, in this research, we efficiently extract protein-protein interaction 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.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.10
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• pp.969-982
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• 2016

Recently, the researches to discovery drug candidates from natural herbs have received considerable attention. In human body, enzyme mostly metabolize the compounds of natural herbs. In this study, we analysis the enzyme interactions using assoication mining. We get this data from BRENDA(: BRaunschweig ENzyme DAtabase) system. Based on enzyme interaction model, we divide the metabolites into substrate metabolites, product metabolites, inhibitor metabolites, and activating metabolites. We then compose substrate metabolite transaction, product metabolite transaction with each metabolites and enzyme interaction transaction with all metabolites. Also we take account of organism for each transactions. We mine frequent metabolites and patterns from six transactions using association rule mining. And we analysis the relationship among metabolites. As a result, we identify the distributions and patterns of metabolites consist in enzyme interactions. We found that metabolites include in only substrate are identified and have very low supports. This results can be useful to develop the effective metabolism prediction model for compounds of natural herbs.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.58 no.2
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• pp.119-127
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• 2013

The interaction between Agrobacterium and soybean has been studied at the transcriptome level but not at the metabolic level. However, it is necessary to investigate the difference in metabolites between susceptible and non-susceptible cultivars for high efficiency transformation. We investigated the difference in metabolites from sonicated soybean cotyledons of Korean cultivars and Bert cultivar. To identify difference in metabolites, sonicated extracts were analysed by Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR/MS). The soybean cultivars were classified by susceptibility using green fluorescent protein expression. We found a difference in metabolites between the high susceptible and low susceptible cultivars. The FT-ICR/MS experimental m/z data of different metabolites were compared with theoretical m/z in KNApSAcK database. The candidate list was made using KNApSAcK and focused on phenolic compounds. These candidate metabolites are speculated to influence factors in the interaction. This list of candidates may be useful to investigate the interaction between Agrobacterium and plants to increase transformation efficiency.

• The Plant Pathology Journal
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• v.24 no.4
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• pp.384-391
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• 2008

Rice blast disease, caused by the pathogenic fungus Magnaporthe grisea, is a serious issue in rice (Oryza sativa L.) growing regions of the world. Transcript profiling in rice inoculated with the fungus has been investigated using the transcriptomics technology, serial analysis of gene expression (SAGE). Short sequence tags containing sufficient information which are ten base-pairs representing the unique transcripts were identified by SAGE technology. We identified a total of 910 tag sequences via the GenBank database, and the resulting genes were shown to be up-regulated in all functional categories under the fungal biotic stress. Compared to the compatible interaction, the stress and defense genes in the incompatible interaction appear to be more up-regulated. Particularly, thaumatin-like gene (TLP) was investigated in determining the gene and protein expression level utilizing Northern and Western blotting analyses, resulting in an increase in both the gene and the protein expression level which arose earlier in the incompatible interaction than in the compatible interaction.

• International journal of advanced smart convergence
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• v.6 no.3
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• pp.29-37
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• 2017

The current behavior recognition system don't match data formats between sensor data measured by user's sensor module or device. Therefore, it is necessary to support data processing, sharing and collaboration services between users and behavior recognition system in order to process sensor data of a large capacity, which is another formats. It is also necessary for real time interaction with users and behavior recognition system. To solve this problem, we propose fog cloud based behavior recognition system for human body sensor data processing. Fog cloud based behavior recognition system solve data standard formats in DbaaS (Database as a System) cloud by servicing fog cloud to solve heterogeneity of sensor data measured in user's sensor module or device. In addition, by placing fog cloud between users and cloud, proximity between users and servers is increased, allowing for real time interaction. Based on this, we propose behavior recognition system for user's behavior recognition and service to observers in collaborative environment. Based on the proposed system, it solves the problem of servers overload due to large sensor data and the inability of real time interaction due to non-proximity between users and servers. This shows the process of delivering behavior recognition services that are consistent and capable of real time interaction.

• Molecular & Cellular Toxicology
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• v.3 no.3
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• pp.208-213
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• 2007

With the help of a development and popularization of microarray technology that enable to us to simultaneously investigate the expression pattern of thousands of genes, the toxicogenomics experimenters can interpret the genome-scale interaction between genes exposed in toxicant or toxicant-related environment. The ultimate and primary goal of toxicogenomics identifies functional context among the group of genes that are differentially or similarly coexpressed under the specific toxic substance. On the other side, public reference databases with transcriptom, proteom, and biological pathway information are needed for the analysis of these complex omics data. However, due to the heterogeneous and independent nature of these databases, it is hard to individually analyze a large omics annotations and their pathway information. Fortunately, several web sites of the public database provide information linked to other. Nevertheless it involves not only approriate information but also unnecessary information to users. Therefore, the systematically integrated database that is suitable to a demand of experimenters is needed. For these reasons, we propose SOP (Search of Omics Pathway) database system which is constructed as the integrated biological database converting heterogeneous feature of public databases into combined feature. In addition, SOP offers user-friendly web interfaces which enable users to submit gene queries for biological interpretation of gene lists derived from omics experiments. Outputs of SOP web interface are supported as the omics annotation table and the visualized pathway maps of KEGG PATHWAY database. We believe that SOP will appear as a helpful tool to perform biological interpretation of genes or proteins traced to omics experiments, lead to new discoveries from their pathway analysis, and design new hypothesis for a next toxicogenomics experiments.