• Bioinformatics and Biosystems
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• v.1 no.2
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• pp.128-133
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• 2006

This paper compares domain combination based protein-protein interaction prediction method with domain based protein-protein interaction method. The prediction accuracy and reliability of the methods are compared using the same prediction technique and interaction data. According to the comparison, domain combination based prediction method has showed superior prediction accuracy to domain based prediction method for protein pairs with fully overlapped domains with protein pairs in learning sets. When we consider that domain combination based method has the effects of assigning a weight to each domain interaction, it implies that we can improve the prediction accuracies of currently available domain or domain combination based protein interaction prediction methods further by developing more advanced weight assignment techniques. Several significant facts revealed from the comparative studies are also described in this paper.

• Journal of KIISE:Software and Applications
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• v.36 no.10
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• pp.851-860
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• 2009

Recently, among the computational methods of protein-protein interaction prediction, vast amounts of domain based methods originated from domain-domain relation consideration have been developed. However, it is true that multi domains collaboration is avowedly ignored because of computational complexity. In this paper, we implemented a protein interaction prediction system based the Interaction Significance matrix, which quantified an influence of domain combination pair on a protein interaction. Unlike conventional domain combination methods, IS matrix contains weighted domain combinations and domain combination pair power, which mean possibilities of domain collaboration and being the main body on a protein interaction. About 63% of sensitivity and 94% of specificity were measured when we use interaction data from DIP, IntAct and Pfam-A as a domain database. In addition, prediction accuracy gradually increased by growth of learning set size, The prediction software and learning data are currently available on the web site.

• 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.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2003.10a
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• pp.7-16
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• 2003

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 pro-bability 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 fur 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 foaming set of interacting protein pairs, very high sensitivity(86%) and specificity(56%) are achieved within our framework.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.5
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• pp.375-379
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• 2009

Protein domain is the conserved unit of compact tree-dimensional structure and evolution, which carries specific function. Domains may appear in patterns in proteins, since they have been conserved through the evolution for functional formation of proteins. In this paper, we propose a formulated method for conservation analysis of domain combination based on association rule. Proposed method measures mutual dependency of domains in a combination, as well as co-occurrence frequency of them, which is conventionally used. Based on the method, we extracted conserve domain combinations in S.cerevisiae proteins and analyzed their functions based on Gene Ontology. From the results, we drew conclusions that domains in S.cerevisiae proteins form patterns whose members are highly affiliated to one another, and that extracted patterns tend to be associated with molecular function. Moreover, the results testified to proposed method superior to conventional ones for identifying domain combinations conserved for functional cooperation.

• Bioinformatics and Biosystems
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• v.1 no.2
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• pp.123-127
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• 2006

TIM barrel domain is widely studied since it is one of most common structure and mediates diverse function maintaining overall structure. TIM barrel domain's function is determined by local structural environment at the C-terminal end of barrel structure. We classified TIM barrel domains by local structural alignment tool, LSHEBA, to understand characteristics of TIM barrel domain's functionalvariation. TIM barrel domains classified as the same cluster share common structure, function and ligands. Over 80% of TIM barrels in clusters share exactly the same catalytic function. Comparing clustering result with that of SCOP, we found that it's important to know local structural environment of TIM barrel domains rather than overallstructure to understand specific structural detail of TIM barrel function. Non TIM barrel domains were associated to make different domain combination to form a different function. The relationship between domain combination, we suggested expected evolutional history. We finally analyzed the characteristics of amino acids around ligand interface.

• Wind and Structures
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• v.35 no.5
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• pp.323-336
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• 2022

This study investigates the applicability of the direct identification of flutter derivatives in the time domain using Rational Function Approximation (RFA), where the extraction procedure requires either a combination of at least two wind speeds or one wind speed. In the frequency domain, flutter derivatives are identified at every wind speed. The ease of identifying flutter derivatives in the time domain creates a paradox because flutter derivative patterns sometimes change in higher-order polynomials. The first step involves a numerical study of RFA extractions for different deck shapes from existing bridges to verify the accurate wind speed combination for the extraction. The second step involves validating numerical simulation results through a wind tunnel experiment using the forced vibration method in one degree of freedom. The findings of the RFA extraction are compared to those obtained using the analytical solution. The numerical study and the wind tunnel experiment results are in good agreement. The results show that the evolution pattern of flutter derivatives determines the accuracy of the direct identification of RFA.

• BMB Reports
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• v.46 no.1
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• pp.53-58
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• 2013

We constructed deletion mutants and seven point mutants by polymerase chain reaction to investigate the specificity of feline foamy virus integrase functional domains. Complementation reactions were performed for three enzymatic activities such as 3'-end processing, strand transfer, and disintegration. The complementation reactions with deletion mutants showed several activities for 3'-end processing and strand transfer. The conserved central domain and the combination of the N-terminal or C-terminal domains increased disintegration activity significantly. In the complementation reactions between deletion and point mutants, the combination between D107V and deletion mutants revealed 3'-end processing activities, but the combination with others did not have any activity, including strand transfer activities. Disintegration activity increased evenly, except the combination with glutamic acid 200. These results suggest that an intact central domain mediates enzymatic activities but fails to show these activities in the absence of the N-terminal or C-terminal domains.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.5
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• pp.427-435
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• 2005

With the accumulation of protein and its related data on the Internet, many domain based computational techniques to predict protein interactions have been developed. However, most of the techniques still have many limitations to be used in real fields. They usually suffer from a low accuracy problem in prediction and do not provide any interaction possibility ranking method for multiple protein pairs. In this paper, we reevaluate a domain combination based protein interaction prediction method and develop an interaction possibility ranking method for multiple protein pairs. Probability equations are devised and proposed in the framework of domain combination based protein interaction prediction method. Using the ranking method, one can discern which protein pair is more probable to interact with each other than other protein pairs in multiple protein pairs. In the validation of the ranking method, we revealed that there exist some correlations between the interacting probability and the precision of the prediction in case of the protein pair group having the matching PIP(Primary Interaction Probability) values in the interacting or non interacting PIP distributions.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1993.10a
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• pp.1147-1156
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• 1993

In order to analyze lateral control in the backward maneuver of a tractor -trailer combination , a kinematic vehicle model and a human operator model with time delay were utilized for the operator/vehicle system. The analysis was carried out using the frequency domain approach. The open-loop stability of the vehicle motion was analyzed through the transfer functions. The sensitivity of the stability of the vehicle motion. to a change in the steering angle, was also analyzed. A mathematical model of the closed -loop operator/vehicle system was then formulated. The closed -loop stability of the operator /vehicle system was then analyzed. The effect of the delay time on the system was also analyzed through computer simulation.