• Journal of KIISE:Software and Applications
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• v.35 no.6
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• pp.347-356
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• 2008

High-throughput microarray is one of the most popular tools in molecular biology, and various computational methods have been developed for the microarray data analysis. While the computational methods easily extract significant features, it suffers from inferring modules of multiple co-regulated genes. Hypernetworhs are motivated by biological networks, which handle all elements based on their combinatorial processes. Hence, the hypernetworks can naturally analyze the biological effects of gene combinations. In this paper, we introduce a hypernetwork classifier for microRNA (miRNA) profile analysis based on microarray data. The hypernetwork classifier uses miRNA pairs as elements, and an evolutionary learning is performed to model the microarray profiles. miTNA modules are easily extracted from the hypernetworks, and users can directly evaluate if the miRNA modules are significant. For experimental results, the hypernetwork classifier showed 91.46% accuracy for miRNA expression profiles on multiple human canters, which outperformed other machine learning methods. The hypernetwork-based analysis showed that our approach could find biologically significant miRNA modules.

• The Journal of Korean Medicine
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• v.35 no.4
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• pp.36-51
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• 2014

Objectives: cDNA microarray is an effective method to snapshot gene expression. Functional clustering of gene expressions can identify herbal medicine mechanisms. Much microarray data is available for various herbal medicines. This study compares regulated genes with herbal medicines to evaluate the nature of the drugs. Methods: Published microarray data were collected. Total RNAs were prepared from dissociated hippocampal dissociate cultures which were given hypoxic shock in the presence of each herbal medicine. Up- or downregulated genes higher than Global M value 0.5 were selected, clustered in functional groups, and compared with various herbal treatments. Results: 1. Akt2 was upregulated by Acorus gramineus SOLAND, Arisaema amurense var. serratum $N_{AKAI}$ and Coptis chinensis $F_{RANCH}$, and they belong to Araceae herb. 2. Nf-${\kappa}b1$, Cd5, $Gn{\gamma}7$ and Sgne1 were upregulated by Arisaema amurense var. serratum $N_{AKAI}$, Coptis chinensis $F_{RANCH}$ and Rheum coreanum $N_{AKAI}$. 3. Woohwangcheongsim-won, Sohaphyang-won and Scutellaria baicalensis $G_{EORGI}$ downregulated Scp2 and upregulated Tsc2. Woohwangcheongsim-won and Sohaphyang-won upregulated Hba1 and downregulated Myf6. 4. Sohaphyang-won and Scutellaria baicalensis $G_{EORGI}$ downregulated Slc12a1. 5. Woohwangcheongsim-won and Arisaema amurense var. serratum $N_{AKAI}$ upregulated $Rar{\alpha}$, Woohwangcheongsim-won and Coptis chinensis $F_{RANCH}$ downregulated Rab5a and $Pdgfr{\alpha}$, and Woohwangcheongsim-won and Rheum coreanum $N_{AKAI}$ upregulated $Plc{\gamma}1$ and downregulated Pla2g1b and Slc10a1. Conclusions: By clustering microarray, genes are commonly identified to be either up- or downregulated. These results will provide new information to understand the efficacy of herbal medicines and to classify them at the molecular level.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.1
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• pp.95-100
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• 2016

Background: Survival time of lymphoma patients can be estimated with the help of microarray technology. In this study, with the use of iterative Bayesian Model Averaging (BMA) method, survival time of Mantle Cell Lymphoma patients (MCL) was estimated and in reference to the findings, patients were divided into two high-risk and low-risk groups. Materials and Methods: In this study, gene expression data of MCL patients were used in order to select a subset of genes for survival analysis with microarray data, using the iterative BMA method. To evaluate the performance of the method, patients were divided into high-risk and low-risk based on their scores. Performance prediction was investigated using the log-rank test. The bioconductor package "iterativeBMAsurv" was applied with R statistical software for classification and survival analysis. Results: In this study, 25 genes associated with survival for MCL patients were identified across 132 selected models. The maximum likelihood estimate coefficients of the selected genes and the posterior probabilities of the selected models were obtained from training data. Using this method, patients could be separated into high-risk and low-risk groups with high significance (p<0.001). Conclusions: The iterative BMA algorithm has high precision and ability for survival analysis. This method is capable of identifying a few predictive variables associated with survival, among many variables in a set of microarray data. Therefore, it can be used as a low-cost diagnostic tool in clinical research.

• The KIPS Transactions:PartD
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• v.16D no.5
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• pp.643-650
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• 2009

As one of typical techniques for acquiring bio-information, microarray has contributed greatly to development of bioinformatics. Although it is established as a core technology in bioinformatics, it has difficulty in sharing and storing data because data from experiments has huge and complex type. In this paper, we propose a new method which uses the feature that microarray data format in MAGE-ML, a standard format for exchanging data, has frequent structurally similar patterns. This method constructs compact database by simplifying MAGE-ML schema. In this method, Inlining techniques and newly proposed classification techniques using structural similarity of elements are used. The structure of database becomes simpler and number of table-joins is reduced, performance is enhanced using this method.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.6
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• pp.44-55
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• 2009

Diagnosis of diseases using gene expression data obtained from microarray chip is an active research area recently. It has been done by general machine learning algorithms, because it is difficult to analyze directly. However, recent research results about the analysis based on the interaction between genes is essential for the gene expression analysis, which means the analysis using the traditional machine learning algorithms has limitations. In this paper, we classify the gene expression data using the hyper-network model that considers the higher-order correlations between the features, and then compares the classification accuracies. And also, we present the new hypo-network model that improve the disadvantage of existing model, and compare the processing performances of the existing hypo-network model based on general sequential processor and the improved hypo-network model implemented on parallel processors. In the experimental results, we show that the performance of our model shows improved and competitive classification performance than traditional machine learning methods, as well as, the existing hypo-network model. We show that the performance is maximized when the hypernetwork model is implemented on our parallel processors.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.9
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• pp.2032-2037
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• 2011

Marker genes are defined as genes in which the expression level characterizes a specific experimental condition. Such genes in which the expression levels differ significantly between different groups are highly informative relevant to the studied phenomenon. In this paper, first the system can detect marker genes that are selected by ranking genes according to statistics after normalizing data with methods that are the most widely used among several normalization methods proposed the while, And it compare and analyze a performance of each of normalization methods with mult-perceptron neural network layer. The Result that apply Multi-Layer perceptron algorithm at Microarray data set including eight of marker gene that are selected using ANOVA method after Lowess normalization represent the highest classification accuracy of 99.32% and the lowest prediction error estimate.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.786-792
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• 2012

A microarray is a collection of thousands of DNAs or RNAs arranged on a substrate, and it enables one to navigate large amounts of gene expression. However, a researcher uses his designed experimental methods to focus on particular phenotypes from the available mass of data. In this paper, we used MicroRNAs(miRNAs) and Protein-Protein Interation(PPI) databases to enhance and expand meanings in microarray data. Further, the expanded data are linked with the Online Mendelian Inheritance in Man(OMIM), and International Statistical Classification of Diseases and Related Health Problems, $10^{th}$ Revision(ICD-10), in order to extract common genetic relationships between diseases. This approach, we expect, should provide new biological views.

• BMB Reports
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• v.40 no.5
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• pp.814-824
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• 2007

Liver cirrhosis (LC) is defined as comprising diffuse fibrosis and regenerating nodules of the liver. The biochemical and anatomical dysfunction in LC results from both reduced liver cell number and portal vascular derangement. Although several studies have investigated dysregulated genes in cirrhotic nodules, little is known about the genes implicated in the pathophysiologic change of LC or about their relationship with the degree of decompensation. Here, we applied cDNA microarray analysis using 38 HBsAg-positive LC specimens to identify the genes dysregulated in HBV-associated LC and to evaluate their relation to disease severity. Among 1063 known cancer- and apoptosis-related genes, we identified 104 genes that were significantly up- (44) or down- (60) regulated in LC. Interestingly, this subset of 104 genes was characteristically correlated with the degree of decompensation, called the Pugh-Child classification (20 Pugh-Child A, 10 Pugh-Child B, and 8 Pugh-Child C). Patient samples from Pugh-Child C exhibited a distinct pattern of gene expression relative to those of Pugh-Child A and B. Especially in Pugh-Child C, genes encoding hepatic proteins and metabolizing enzymes were significantly down-regulated, while genes encoding various molecules related to cell replication were up-regulated. Our results suggest that subsets of genes in liver cells correspond to the pathophysiologic change of LC according to disease severity and possibly to hepatocarcinogenesis.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.3C
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• pp.112-118
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• 2005

In this paper, we propose a new nonlinear matching measure for automatic analysis of the on-off type DNA microarray images in which the hybridized spots are detected by the template matching method. The proposed measure is obtained by binary-thresholding over the whole template region and taking the number of white pixels inside the spotted area. This measure is compared with the normalized covariance in terms of the classification ability of the successfulness of the locating markers. The proposed measure is evaluated for the scanned images of HPV DNA microarrays where the marker locating is a critical issue because of the small number of spots. The targeting spots of HPV DNA chips are designed for genotyping 22 types of the human papilloma virus(HPV). The proposed measure is proven to give more discriminative response reducing the miss cases of the successful marker locating.

• Journal of Genetic Medicine
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• v.18 no.1
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• pp.31-37
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• 2021

Purpose: To summarize the results of chromosomal microarray analysis (CMA) for copy number variants (CNVs) detection and clinical utility in a single tertiary hospital. Materials and Methods: We performed CMA in 46 patients over the course of two years. Detected CNVs were classified into five categories according to the American College of Medical Genetics and Genomics guidelines and correlated with clinical manifestations. Results: A total of 31 CNVs were detected in 19 patients, with a median CNV number per patient of two CNVs. Among these, 16 CNVs were classified as pathogenic (n=3) or likely pathogenic (LP) (n=11) or variant of uncertain significance (n=4). The 16p11.2 deletion and 16p13.11 deletion classified as LP were most often detected in 6.5% (3/46), retrospectively. CMA diagnostic yield was 24.3% (9/37 patients) for symptomatic patients. The CNVs results of the commercial newborn screening test using next generation sequencing platforms showed high concordance with CMA results. Conclusion: CMA seems useful as a first-tier test for developmental delay with or without congenital anomalies. However, the classification and interpretation of CMA still remained a challenge. Further research is needed for evidence-based interpretation.