• 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: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.

• Bulletin of the Korean Chemical Society
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• v.20 no.10
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• pp.1136-1144
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• 1999

The collision-induced reaction of gas-phase atomic hydrogen with hydrogen atoms chemisorbed on a silicon (001)-(2×1) surface is studied by use of the classical trajectory approach. The model is based on reaction zone atoms interacting with a finite number of primary system silicon atoms, which then are coupled to the heat bath, i.e., the bulk solid phase. The potential energy of the Hads‥Hgas interaction is the primary driver of the reaction, and in all reactive collisions, there is an efficient flow of energy from this interaction to the Hads-Si bond. All reactive events occur on a subpicosecond scale, following the Eley-Rideal mechanism. These events occur in a localized region around the adatom site on the surface. The reaction probability shows the maximum near 700K as the gas temperature increases, but it is nearly independent of the surface temperature up to 700 K. Over the surface temperature range of 0-700 K and gas temperature range of 300 to 2500 K, the reaction probability lies at about 0.1. The reaction energy available for the product states is small, and most of this energy is carried away by the desorbing H2 in its translational and vibrational motions. The Langevin equation is used to consider energy exchange between the reaction zone and the bulk solid phase.

• Journal of Korea Multimedia Society
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• v.11 no.3
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• pp.301-314
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• 2008

The existing event recognition is accomplished with the limited systematic foundation, and thus much longer learning time is needed for emergency scenario interpretation due to large scale of probability data. In this paper, we propose a method for nile-based event recognition of an independent object(human) which extract a feature vectors from the object and analyze the behavior pattern of each object and interpretation of emergency scenarios using a probability and object's events. The event rule of an independent object is composed of the Primary-event, Move-event, Interaction-event, and 'FALL DOWN' event and is defined through feature vectors of the object and the segmented motion orientated vector (SMOV) in which the dynamic Bayesian network is applied. The emergency scenario is analyzed using current state of an event and its post probability. In this paper, we define diversified events compared to that of pre-existing method and thus make it easy to expand by increasing independence of each events. Accordingly, semantics information, which is impossible to be gained through an.

• Korea journal of population studies
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• v.17 no.2
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• pp.77-97
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• 1994

Despite the fact that the national family planning program in korea has accomplished its primary goals of fertility reduction and universal contraceptive use, the induced abortion is still high and there has been an increasing trend in the population sex ratio at birth. It seems that the changes in the imbalance of sex ratio have originated from traditonal boy preference. This indicates that much of the current family planning program can be overhauled, so that the program quality could be better controlled, by preventing the number of unwanted pregnancies and the imbalance of sex ratios. This paper aims, therefore, to examine the determinants of induced abortion through the investigation of pregnancy outcomes and their changes over time and to study the interaction between induced abortion, boy preference and the imbalance of sex ratio in Korea. The abortion rate had increased rapidly until the mid-1980s when there were about the same number of abortions as live births. Thereafter, the abortion rate has been maintained at this high level. By parity it shows a much higher abortion rate for a higher parity at all time. From the first parity, the sex composition of previous children stands out as the most important factor in deciding the pregnancy outcome at all time. The probability of a pregnancy ending in an abortion increases substantially when parents already had a son. The decline of the desired family size and the sustained strong son preference has made the sex of children a more important factor in the determination of the pregnancy outcome. Women's education has had consistently positive effects on the probability of a pregnancy ending in an abortion, but the effect shows a steady decline over time. The premarital pregnancy and urban residence also increase the abortion probability. This study suggests that the main concerns of the family planning program should be to strengthen the social support policies so as to weaken the son preference value leading to a balanced sex ratio and prevention of induced abortions.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.6
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• pp.503-513
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• 2005

With the recognition of the importance of computational approach for protein-protein interaction prediction, many techniques have been developed to computationally predict protein-protein interactions. However, few techniques are actually implemented and announced in service form for general users to readily access and use the techniques. In this paper, we design and implement a protein interaction prediction service system based on the domain combination based protein-protein interaction prediction technique, which is known to show superior accuracy to other conventional computational protein-protein interaction prediction methods. In the prediction accuracy test of the method, high sensitivity($77\%$) and specificity($95\%$) are achieved for test protein pairs containing common domains with teaming sets of proteins in a Yeast. The stability of the method is also manifested through the testing over DIP CORE, HMS-PCI, and TAP data. Performance, openness and flexibility are the major design goals and they are achieved by adopting parallel execution techniques, web Services standards, and layered architecture respectively. In this paper, several representative user interfaces of the system are also introduced with comprehensive usage guides.