• The KIPS Transactions:PartB
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• v.13B no.1 s.104
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• pp.1-6
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• 2006

The prediction of protein function basically make use of a protein-protein interaction map based on the concept of guilt-by-association. The method however cannot determine the functions of proteins in case that the target protein does not interact with proteins with known functions directly. This paper studies protein function prediction considering the given problem as a K-class classification problem and proposes a predictive approach utilizing a modular neural network. The proposed method uses interaction data and protein related attributes as well. The experimental results demonstrate that the proposed approach can predict the functional roles of Yeast proteins whose interaction knowledge is not known and shows better performance than the graph-based models that use protein interaction data.

• Proceedings of the Korean Biophysical Society Conference
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• 2003.06a
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• pp.65-65
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• 2003

After many genome projects, algorithms and software to process explosively growing biological information have been developed. To process huge amount of biological information, high performance computing equipments are essential. If we use the remote resources such as computing power, storages etc., through a Grid to share the resources in the Internet environment, we will be able to obtain great efficiency to process data at a low cost. Here we present the performance improvement of the protein secondary structure prediction (PSIPred) by using the Grid platform, distributing protein sequence data on the Grid where each computer node analyzes its own part of protein sequence data to speed up the structure prediction. On the Grid, genome scale secondary structure prediction for Mycoplasma genitalium, Escherichia coli, Helicobacter pylori, Saccharomyces cerevisiae and Caenorhabditis slogans were performed and analyzed by a statistical way to show the protein structural deviation and comparison between the genomes. Experimental results show that the Grid is a viable platform to speed up the protein structure prediction and from the predicted structures.

• 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.

• 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.

• BMB Reports
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• v.42 no.11
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• pp.697-704
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• 2009

Membrane proteins play important roles in the biology of the cell, including intercellular communication and molecular transport. Their well-established importance notwithstanding, the high-resolution structures of membrane proteins remain elusive due to difficulties in protein expression, purification and crystallization. Thus, accurate prediction of membrane protein topology can increase the understanding of membrane protein function. Here, we provide a brief review of the diverse computational methods for predicting membrane protein structure and function, including recent progress and essential bioinformatics tools. Our hope is that this review will be instructive to users studying membrane protein biology in their choice of appropriate bioinformatics methods.

• Proceedings of the KSRS Conference
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• v.2
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• pp.812-815
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• 2006

Proteins are essential agents for controlling, effecting and modulating cellular functions, and proteins with similar sequences have diverged from a common ancestral gene, and have similar structures and functions. Function prediction of unknown proteins remains one of the most challenging problems in bioinformatics. Recently, various computational approaches have been developed for identification of short sequences that are conserved within a family of closely related protein sequence. Protein function is often correlated with highly conserved motifs. Motif is the smallest unit of protein structure and function, and intends to make core part among protein structural and functional components. Therefore, prediction methods using data mining or machine learning have been developed. In this paper, we describe an approach for protein function prediction of motif-based models using data mining. Our work consists of three phrases. We make training and test data set and construct classifier using a training set. Also, through experiments, we evaluate our classifier with other classifiers in point of the accuracy of resulting classification.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.7 s.250
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• pp.841-848
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• 2006

Many researchers are developing computational prediction methods for protein tertiary structures to get much more information of protein. These methods are very attractive on the aspects of breaking technologies of computer hardware and simulation software. One of the computational methods for the prediction is a fragment assembly method which shows good ab initio predictions at several cases. There are many barriers, however, in conventional fragment assembly methods. Argues on protein energy functions and global optimization to predict the structures are in progress fer example. In this study, a new prediction method for protein structures is proposed. The proposed method mainly consists of two parts. The first one is a fragment assembly which uses very shot fragments of representative proteins and produces a prototype of a given sequence query of amino acids. The second one is a global optimization which folds the prototype and makes the only protein structure. The goodness of the proposed method is shown through numerical experiments.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.15 no.3
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• pp.191-199
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• 2011

In this paper, we adopt a position specific scoring matrix as an abstraction of amino acid type to derive two new statistical potentials for protein structure prediction, and investigated its effect on the quality of the potentials compared to that derived using residue specific amino acid identity. For stringent test of the potential quality, we carried out folding simulations of 91 residue A chain of protein 2gpi, and found unexpectedly that the abstract amino acid type improved the quality of the one-body type statistical potential, but not for the two-body type statistical potential which describes long range interactions. This observation could be effectively used when one develops more accurate potentials for structure prediction, which are usually involved in merging various one-body and many-body potentials.

• Journal of the Korean Data and Information Science Society
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• v.15 no.4
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• pp.1019-1031
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• 2004

This paper describes a new modeling method for the prediction of transmembrane protein topology. The structural regions of the transmembrane protein have been modeled by means of a multiple state hidden Markov model that has provided for the detailed modeling of the heterogeneous amino acid distributions of each structural region. Grammatical constraints have been incorporated to the prediction method in order to capture the biological order of membrane protein topology. The proposed method correctly predicted 76% of all membrane spanning regions and 92% sidedness of the integration when all membrane spanning regions were found correctly.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.7
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• pp.1749-1756
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• 2014

Knowledge about protein subcellular localization provides important information about protein function. This paper improves a label power-set multi-label classification for the accurate prediction of subcellular localization of proteins which simultaneously exist at multiple subcellular locations. Among multi-label classification methods, label power-set method can effectively model the correlation between subcellular locations of proteins performing certain biological function. With constrained optimization, this paper calculates combination weights which are used in the linear combination representation of a multi-label by other multi-labels. Using these weights, the prediction probabilities of multi-labels are combined to give final prediction results. Experimental results on human protein dataset show that the proposed method achieves higher performance than other prediction methods for protein subcellular localization. This shows that the proposed method can successfully enrich the prediction probability of multi-labels by exploiting the overlapping information between multi-labels.