• The KIPS Transactions:PartB
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• v.18B no.2
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• pp.67-72
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• 2011

Single nucleotide polymorphisms (SNPs) are a very important tool for the study of human genome structure. Cluster analysis of the large amount of gene expression data is useful for identifying biologically relevant groups of genes and for generating networks of gene-gene interactions. In this paper we compared the clusters of SNPs within asthma group and normal control group obtained by using hierarchical cluster analysis method with entropy distance. It appears that the 5-cluster collections of the two groups are significantly different. We searched the best set of SNPs that are useful for diagnosing the two types of asthma using representative SNPs of the clusters of the asthma group. Here support vector machines are used to evaluate the prediction accuracy of the selected combinations. The best combination model turns out to be the five-locus SNPs including one on the gene ALOX12 and their accuracy in predicting aspirin tolerant asthma disease risk among asthmatic patients is 66.41%.

• Journal of KIISE:Databases
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• v.34 no.1
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• pp.46-58
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• 2007

Since microarray data acquire tens of thousands of gene expression values simultaneously, they could be very useful in identifying the phenotypes of diseases. However, the results of analyzing several microarray datasets which were independently carried out with the same biological objectives, could turn out to be different. One of the main reasons is attributable to the limited number of samples involved in one microarry experiment. In order to increase the classification accuracy, it is desirable to augment the sample size by integrating and maximizing the use of independently-conducted microarray datasets. In this paper, we propose a novel two-stage approach which firstly integrates individual microarray datasets to overcome the problem caused by limited number of samples, and identifies informative genes, secondly builds a classifier using only the informative genes. The classifier from large samples by integrating independent microarray datasets achieves high accuracy up to 24.19% increase as against other comparison methods, sensitivity, and specificity on independent test sample dataset.

• Microbiology and Biotechnology Letters
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• v.34 no.2
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• pp.129-135
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• 2006

A gene encoding for a putative microsomal epoxide hydrolase (mEH) of a zebrafish, Danio rerio, was cloned and characterized. The putative mEH protein of D. rerio exhibited sequence similarity with mammalian mEH and some other bacterial EHs. A structural model for the putative mEH was constructed using homology modeling based on the crystallographic templates, 1 qo7 and 1 ehy. The catalytic triad consisting of $Asp^{233}$, $Glu^{413}$, and $His^{440}$ was identified, and the characteristic features such as two tyrosine residues and oxyanion hole were found to be highly conserved. Based on bioinformatic analysis together with EH activity assay, the putative protein was annotated as mEH of D. rerio. Enantiopure styrene oxide with enantiopurity of 99%ee and yield of 33.5% was obtained from racemic styrene oxide by the enantioselective hydrolysis activity of recombinant mEH of D. rerio for 45 min.

• Journal of KIISE:Software and Applications
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• v.29 no.11
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• pp.775-784
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• 2002

Microarray data, obtained from DNA chip technologies, is the measurement of the expression level of thousands of genes in cells or tissues. It is used for gene function prediction or cancer diagnosis based on gene expression patterns. Among diverse methods for data analysis, the Bayesian network represents the relationships among data attributes in the form of a graph structure. This property enables us to discover various relations among genes and the characteristics of the tissue (e.g., the cancer type) through microarray data analysis. However, most of the present microarray data sets are so sparse that it is difficult to apply general analysis methods, including Bayesian networks, directly. In this paper, we harness an efficient structural learning algorithm and data dimensionality reduction in order to analyze microarray data using Bayesian networks. The proposed method was applied to the analysis of real microarray data, i.e., the NC160 data set. And its usefulness was evaluated based on the accuracy of the teamed Bayesian networks on representing the known biological facts.

• KIISE Transactions on Computing Practices
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• v.23 no.1
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• pp.28-36
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• 2017

After the genome projects of the 90s, a vast number of gene studies have been stored in online databases. By using these databases, several biological relationships can be inferred. In this study, we proposed a method to infer disease-gene relationships using title and body in biomedical text. The title was used to extract hub genes from data in the literature; whereas, the body of the literature was used to extract sub genes that are related to hub genes. Through these steps, we were able to construct a local gene-network for each report in the literature. By integrating the local gene-networks, we then constructed a global gene-network. Subsequent analyses of the global gene-network allowed inference of disease-related genes with high rank. We validated the proposed method by comparing with previous methods. The results indicated that the proposed method is a meaningful approach to infer disease-related genes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.4
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• pp.992-999
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• 2014

This paper presents an extensive experimental comparison of a variety of multi-label learning methods for the accurate prediction of subcellular localization of proteins which simultaneously exist at multiple subcellular locations. We compared several methods from three categories of multi-label classification algorithms: algorithm adaptation, problem transformation, and meta learning. Experimental results are analyzed using 12 multi-label evaluation measures to assess the behavior of the methods from a variety of view-points. We also use a new summarization measure to find the best performing method. Experimental results show that the best performing methods are power-set method pruning a infrequently occurring subsets of labels and classifier chains modeling relevant labels with an additional feature. futhermore, ensembles of many classifiers of these methods enhance the performance further. The recommendation from this study is that the correlation of subcellular locations is an effective clue for classification, this is because the subcellular locations of proteins performing certain biological function are not independent but correlated.

• Journal of Korea Society of Industrial Information Systems
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• v.17 no.6
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• pp.59-72
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• 2012

Since a protein displays its specific functions when disorder region of protein sequence transits to order region with provoking a biological reaction, the separation of disorder region and order region from the sequence data is urgently necessary for predicting three dimensional structure and characteristics of the protein. To classify the disorder and order region efficiently, this paper proposes a classification/prediction method using sequence data while acquiring a non-biased result on a specific characteristics of protein and improving the classification speed. The emerging patterns based EPs-TFP methods utilizes only the essential emerging pattern in which the redundant emerging patterns are removed. This classification method finds the sequence patterns of disorder region, such sequence patterns are frequently shown in disorder region but relatively not frequently in the order region. We expand P-tree and T-tree conceptualized TFP method into a classification/prediction method in order to improve the performance of the proposed algorithm. We used Disprot 4.9 and CASP 7 data to evaluate EPs-TFP technique, the results of order/disorder classification show sensitivity 73.6, specificity 69.51 and accuracy 74.2.

• The KIPS Transactions:PartD
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• v.10D no.6
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• pp.897-906
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• 2003

The traditional animal phylogenetic tree is to align the body structure of the animal phylums from simple to complex based on the initial development character. Currently, molecular systematics research based on the molecular, it is on the fly, is again estimating prior trend and show the new genealogy and interest of the evolution. In this paper, we generate the training set which is obtained from a DNA sequence ans apply to the classification. We made use of the mitochondrial DNA for the experiment, and then proved the accuracy using the MEGA program which is anaysis program, it is used in the biology field. Although the result of the mining has to proved through biological experiment, it can provede the methodology for the efficient classify and can reduce the time and effort to the experiment.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.5
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• pp.551-555
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• 2010

In protein 2-DE image analysis, the accuracy of spot-matching operation which identifies the spot of the same protein in each 2-DE gel image is intensively influenced by the errors caused by the various experimental conditions. This paper proposes an efficient method to find more accurate spot-matching patterns based on multiple reference gel images in spot-matching pattern analysis in protein 2-DE image analysis. Additionally, in order to improve the reduce the execution time which is increased exponentially along with the increasing number of gel images, a "partition then extension" framework is used to find spot-matching pattern of long length and of higher accuracy. In the experiments on real 2-DE images of human liver tissue are used to confirm the accuracy and the efficiency of the proposed algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.581-582
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• 2010

This paper performed the basic study for developing the Photodynamic Therapy Equipment for medical treatment. We developed the equipment palpating cell proliferation using a high brightness LED. This equipment was fabricated using a micro-controller and a high brightness LED, and designed to enable us to control light irradiation time, intensity, frequency and so on. Especially, to control the light irradiation frequency, FPGA was used, and to control the change of output value, TLC5941 was used. Control stage is divided into 30 step by program. Consequently, the current value could be controlled by the change of level in Continue Wave(CW) and Pulse Width Modulation(PWM), and the output of a high brightness LED could be controlled stage by stage.