• KIPS Transactions on Software and Data Engineering
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• v.2 no.12
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• pp.899-902
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• 2013

In this paper, we present a method to visualize the contact region between two molecules in a protein complex in a threedimensional space. The contact region of two molecules shows compatibility in geometric aspects. Usually, the computation of the area of contact region has been used to show the strength of compatibility. The numerical value and simple drawing of contact region would be useful for comparing the relative strength of different contacts, but it is not appropriate for analysing the geometric characteristics of the contact region. In this paper, we present a method to show the compatibility between two molecules by visualizing the distance information between them.

• Korean Journal of Cognitive Science
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• v.28 no.4
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• pp.299-314
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• 2017

Predicting compound-protein interactions in-silico is significant for the drug discovery. In this paper, we propose an scalable machine learning model to predict compound-protein interaction. The key idea of this scalable machine learning model is the architecture of pairwise neural network model and feature embedding method from the raw data, especially for protein. This method automatically extracts the features without additional knowledge of compound and protein. Also, the pairwise architecture elevate the expressiveness and compact dimension of feature by preventing biased learning from occurring due to the dimension and type of features. Through the 5-fold cross validation results on large scale database show that pairwise neural network improves the performance of predicting compound-protein interaction compared to previous prediction models.

• 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.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.60 no.1
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• pp.33-40
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• 2015

Soybean is very useful crop to supply vegetable protein for human. Supply of soybean is increased because it has useful ingredient. Recently, cultivation of soybean in paddy field is increasing due to the increase of rice stockpile in Korea. Hence, in this study, expression of protein was identified regarding different environment for cultivation to investigate the effect of different environment on protein expression. Two-dimensional electrophoresis was performed to investigate the expression of protein using image analysis program to measure degree of protein expression in numerical value. Hannam-kong, Beakcheon-Kong, Hwangkeum-Kong, and Danwon-Kong were used as plant material. 2-DE combined with image analysis revealed that each degree of protein expression of Hannam-Kong and Hwangkeum-Kong in upland field was higher than degree of protein expression in paddy field. However, in case of Beackcheon-Kong, the phenomenon was opposite. In Danwon-kong, the degree of protein expression was not different between up-land field and paddy field. To this end, major protein spots were not different between paddy field and upland field among all cultivars. It could be suggested that protein expression is not severely different by various environment, but different environment affects degree of protein expression.