• 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: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.

• 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.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.

• International Journal of Fluid Machinery and Systems
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• v.7 no.4
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• pp.130-141
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• 2014

As a high specific speed pump, the contra-rotating axial flow pump with two rotors rotating reversely has been proved with higher hydraulic and cavitation performance, while in our previous researches, the potential interaction between two blade rows was distinctly observed for our prototype rotors designed with equal rotational speed for both front and rear rotors. Based on the theoretical and experimental evidences, a rotational speed optimization methodology was proposed and applied in the design of a new combination of contra-rotating rotors, primarily in expectation of the optimized blade pressure distributions as well as pertinently improved hydraulic performances including cavitation performance. In the present study, given one stationary and two rotating frames in the contra-rotating rotors case, a pressure measurement concept taking account of the revolutions of both front and rear rotors simultaneously was adopted. The casing wall pressure data sampled in time domain was successfully transferred into space domain, by which the ensemble averaged blade-to-blade pressure distributions at the blade tip of two contra-rotating rotors under different operation conditions were studied. It could be seen that the rotor pair with the optimized rotational speed combination as well as work division, shows more reasonable blade-to-blade pressure distribution and well weakened potential interaction. Moreover, combining the loading curves estimated by the measured casing wall pressure, the cavitation performance of the rotor pairs with new rotational speed combination were proved to be superior to those of the prototype pairs.

• BMB Reports
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• v.31 no.4
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• pp.370-376
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• 1998

Src-Homology 2 (SH2) domains have a capacity to bind phosphotyrosine-containing sequence context and play essential roles in various cellular signaling pathways. Due to the specific nature of the binding between SH2 domains and their counterpart proteins, inhibitors of SID domain binding have drawn extensive attention as a potential candidate for therapeutic agents. Here, we describe the binding assay system to screen for the ligands or blockers of the SH2 domains with an emphasis on the $p56^{lck}$ SH2 domain. In our assay system, SID domains expressed and purified as fusion proteins to Glutathione-S-transferase (GST) were covalently attached to 96-well microtitre plates through amide bond formation, which were subsequently allowed to bind the biotinylated phosphotyrosine (pY)containing synthetic pep tides. The binding of biotinylated pY peptides was detected by the horseradish peroxidase (HRP)-conjugated streptavidin. Using the various combinations of SH2 domain-pY peptides, we observed that: (1) The binding of pY-peptides to its counterpart SH2 domain is concentration-dependent and saturable; (2) The binding is highly specific for a particular combination of SH2 domain-pY peptide pair; and (3) The binding of Lck SH2-cognate pY-peptides is specifically competed by the nonbiotinylated peptides with expected relative affinity. These results indicate that the established assay system detects the SH2-pY peptide interaction with reproducible sensitivity and specificity and is suitable for screening the specific inhibitors of $p56^{lck}$ SH2 function.