• Journal of Institute of Control, Robotics and Systems
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• v.10 no.12
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• pp.1172-1180
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• 2004

Recently, the gene expression data, product of high-throughput technology, appeared in earnest and the studies related with it (so-called bioinformatics) occupied an important position in the field of biological and medical research. The microarray is a revolutionary technology which enables us to monitor several thousands of genes simultaneously and thus to gain an insight into the phenomena in the human body (e.g. the mechanism of cancer progression) at the molecular level. To obtain useful information from such gene expression measurements, it is essential to analyze the data with appropriate techniques. However the high-dimensionality of the data can bring about some problems such as curse of dimensionality and singularity problem of matrix computation, and hence makes it difficult to apply conventional data analysis methods. Therefore, the development of method which can effectively treat the data becomes a challenging issue in the field of computational biology. This research focuses on the gene selection and classification for cancer subtype discrimination based on gene expression (microarray) data.

• Genomics & Informatics
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• v.7 no.2
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• pp.122-130
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• 2009

A systems biology approach for the identification of perturbed molecular functions is required to understand the complex progressive disease such as breast cancer. In this study, we analyze the microarray data with Gene Ontology terms of molecular functions to select perturbed molecular functional modules in breast cancer tissues based on the definition of Gene ontology Functional Code. The Gene Ontology is three structured vocabularies describing genes and its products in terms of their associated biological processes, cellular components and molecular functions. The Gene Ontology is hierarchically classified as a directed acyclic graph. However, it is difficult to visualize Gene Ontology as a directed tree since a Gene Ontology term may have more than one parent by providing multiple paths from the root. Therefore, we applied the definition of Gene Ontology codes by defining one or more GO code(s) to each GO term to visualize the hierarchical classification of GO terms as a network. The selected molecular functions could be considered as perturbed molecular functional modules that putatively contributes to the progression of disease. We evaluated the method by analyzing microarray dataset of breast cancer tissues; i.e., normal and invasive breast cancer tissues. Based on the integration approach, we selected several interesting perturbed molecular functions that are implicated in the progression of breast cancers. Moreover, these selected molecular functions include several known breast cancer-related genes. It is concluded from this study that the present strategy is capable of selecting perturbed molecular functions that putatively play roles in the progression of diseases and provides an improved interpretability of GO terms based on the definition of Gene Ontology codes.

• Communications for Statistical Applications and Methods
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• v.29 no.5
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• pp.591-601
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• 2022

In gene set analysis with microarray expression data, a group of genes such as a gene regulatory pathway and a signaling pathway is often tested if there exists either differentially expressed (DE) or differentially co-expressed (DC) genes between two biological conditions. Recently, a statistical test based on covariance estimation have been proposed in order to identify DC genes. In particular, covariance regularization by hard thresholding indeed improved the power of the test when the proportion of DC genes within a biological pathway is relatively small. In this article, we compare covariance thresholding methods using four different regularization penalties such as lasso, hard, smoothly clipped absolute deviation (SCAD), and minimax concave plus (MCP) penalties. In our extensive simulation studies, we found that both SCAD and MCP thresholding methods can outperform the hard thresholding method when the proportion of DC genes is extremely small and the number of genes in a biological pathway is much greater than a sample size. We also applied four thresholding methods to 3 different microarray gene expression data sets related with mutant p53 transcriptional activity, and epithelium and stroma breast cancer to compare genetic pathways identified by each method.

• Biomedical Science Letters
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• v.15 no.3
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• pp.261-263
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• 2009

Methylprednisolone is a synthetic glucocorticoid which is usually taken intravenously for many neurosurgical diseases which cause edema including brain tumor, and trauma including spinal cord injury. Methylprednisolone reduces swelling and decreases the body's immune response. It is also used to treat many immune and allergic disorders, such as arthritis, lupus, psoriasis, asthma, ulcerative colitis, and Crohn's disease. To identify genes expressed during methylprednisolone treatment against neurons of rats (PC12 cells), DNA microarray method was used. We have isolated 2 gene groups (up- or down-regulated genes) which are methylprednisolone differentially expressed in neurons. Lipocalin 3 is the gene most significantly increased among 772 up-regulated genes (more than 2 fold over-expression) and Aristaless 3 is the gene most dramatically decreased among 959 down-regulated genes (more than 2 fold down-expression). The gene increased expression of Fgb, Thbd, Cfi, F3, Kngl, Serpinel, C3, Tnfrsf4 and Il8rb are involved stress-response gene, and Nfkbia, Casp7, Pik3rl, I11b, Unc5a, Tgfb2, Kitl and Fgf15 are strongly associated with development. Cell cycle associated genes (Mcm6, Ccnb2, Plk1, Ccnd1, E2f1, Cdc2a, Tgfa, Dusp6, Id3) and cell proliferation associated genes (Ccl2, Tnfsf13, Csf2, Kit, Pim1, Nr3c1, Chrm4, Fosl1, Spp1) are down-regulated more than 2 times by methylprednisolone treatment. Among the genes described above, 4 up-regulated genes are confirmed those expression by RT-PCR. We found that methylprednisolone is related to expression of many genes associated with stress response, development, cell cycle, and cell proliferation by DNA microarray analysis. However, We think further experimental molecular studies will be needed to figure out the exact biological function of various genes described above and the physiological change of neuronal cells by methylprednisolone. The resulting data will give the one of the good clues for understanding of methylprednisolone under molecular level in the neurons.

• Genomics & Informatics
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• v.4 no.4
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• pp.161-166
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• 2006

The establishment of DNA microarray technology has enabled high-throughput analysis and molecular profiling of various types of cancers. By using the gene expression data from microarray analysis we are able to investigate diagnostic applications at the molecular level. The most important step in the application of microarray technology to cancer diagnostics is the selection of specific markers from gene expression profiles. In order to select markers of Immortalization and transformation we used c-myc and $H-ras^{V12}$ oncogene-transfected NIH3T3 cells as our model system. We have identified 8751 differentially expressed genes in the immortalization/transformation model by multivariate permutation F-test (95% confidence, FDR<0.01). Using the support vector machine algorithm, we selected 13 discriminative genes which could be used to predict immortalization and transformation with perfect accuracy. We assayed $H-ras^{V12}$-transfected 'transformed' cells to validate our immortalization/transformation dassification system. The selected molecular markers generated valuable additional information for tumor diagnosis, prognosis and therapy development.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.8
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• pp.3543-3549
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• 2015

Background: Bladder cancer is one of the most common cancers worldwide. Gene expression profiling using microarray technologies improves the understanding of cancer biology. The aim of this study was to determine the gene expression profile in Egyptian bladder cancer patients. Materials and Methods: Samples from 29 human bladder cancers and adjacent non-neoplastic tissues were analyzed by cDNA microarray, with hierarchical clustering and multidimensional analysis. Results: Five hundred and sixteen genes were differentially expressed of which SOS1, HDAC2, PLXNC1, GTSE1, ULK2, IRS2, ABCA12, TOP3A, HES1, and SRP68 genes were involved in 33 different pathways. The most frequently detected genes were: SOS1 in 20 different pathways; HDAC2 in 5 different pathways; IRS2 in 3 different pathways. There were 388 down-regulated genes. PLCB2 was involved in 11 different pathways, MDM2 in 9 pathways, FZD4 in 5 pathways, p15 and FGF12 in 4 pathways, POLE2 in 3 pathways, and MCM4 and POLR2E in 2 pathways. Thirty genes showed significant differences between transitional cell cancer (TCC) and squamous cell cancer (SCC) samples. Unsupervised cluster analysis of DNA microarray data revealed a clear distinction between low and high grade tumors. In addition 26 genes showed significant differences between low and high tumor stages, including fragile histidine triad, Ras and sialyltransferase 8 (alpha) and 16 showed significant differences between low and high tumor grades, like methionine adenosyl transferase II, beta. Conclusions: The present study identified some genes, that can be used as molecular biomarkers or target genes in Egyptian bladder cancer patients.

• The Korean Journal of Nuclear Medicine
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• v.37 no.1
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• pp.43-52
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• 2003

Microarray technology allows the simultaneous analysis of gene expression patterns of thousands of genes, in a systematic fashion, under a similar set of experimental conditions, thus making the data highly comparable. In some cases arrays are used simply as a primary screen loading to downstream molecular characterization of individual gene candidates. In other cases, the goal of expression profiling is to begin to identify complex regulatory networks underlying developmental processes and disease states. Microarrays were originally used with ceil lines or other simple model systems. More recently, microarrays have been used in the analysis of more complex biological tissues including neural systems and the brain. The application of cDNA arrays in neuropsychiatry has lagged behind other fields for a number of reasons. These include a requirement for a large amount of input probe RNA In fluorescent-glass based array systems and the cellular complexity introduced by multicellular brain and neural tissues. An additional factor that impacts the general use of microarrays in neuropsychiatry is the lack of availability of sequenced clone sets from model systems. While human cDNA clones have been widely available, high qualify rat, mouse, and drosophilae, among others are just becoming widely available. A final factor in the application of cDNA microarrays in neuropsychiatry is cost of commercial arrays. As academic microarray facilitates become more commonplace custom made arrays will become more widely available at a lower cost allowing more widespread applications. in summary, microarray technology is rapidly having an impact on many areas of biomedical research. Radioisotope-nylon based microarrays offer alternatives that may in some cases be more sensitive, flexible, inexpensive, and universal as compared to other array formats, such as fluorescent-glass arrays. In some situations of limited RNA or exotic species, radioactive membrane microarrays may be the most practical experimental approach in studying psychiatric and neurodegenerative disorders, and other complex questions in the brain.

• Journal of the Korean Data and Information Science Society
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• v.17 no.2
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• pp.311-324
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• 2006

The Support Vector Machine(SVM) is very functional and efficient classification method to any other classification analysis method. However, its optimal extension to more than two classes is not obvious. In this paper several multi-category SVM methods are introduced and compared using simulation and real data sets. Also comparison with traditional multi-category classification and SVM based methods is performed.

• BMB Reports
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• v.41 no.9
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• pp.670-677
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• 2008

A cDNA microarray composed of 2,028 different ESTs from two shrimp species, Penaeus monodon and Masupenaeus japonicus, was employed to identify yellow head virus (YHV)-responsive genes in hemocytes of P. monodon. A total of 105 differentially expressed genes were identified and grouped into five different clusters according to their expression patterns. One of these clusters, which comprised five genes including cathepsin L-like cysteine peptidase, hypothetical proteins and unknown genes, was of particular interest because the transcripts increased rapidly ($\leq$ 0.25 hours) and reached high expression levels in response to YHV injection. Microarray data were validated by realtime RT-PCR analyses of selected differentially expressed transcripts. In addition, comparative analysis of the hemocyte transcription levels of three of these genes between surviving and non-surviving shrimp revealed significantly higher expression levels in surviving shrimp.

• Bulletin of the Korean Chemical Society
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• v.30 no.2
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• pp.323-326
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• 2009

SR proteins are essential splicing regulators and also modulate alternative splicing events, which function both as redundant and substrate-specific manner. The Drosophila B52/SRp55, a member of the SR protein family, is essential for the fly development in vivo, as deletion of B52 gene results in lethality of animals at the second instar larval stage. Identification of the splicing target genes of B52 thus should be crucial for the understanding of the specific developmental role of B52 in vivo. In this study, we performed whole-genome DNA microarray experiments with a B52- knock-out animal. Analysis of the microarray data not only provided the B52-dependent gene expression signature, but also revealed a larval-stage specific, alternative splicing target gene of B52. Our result thus provides a starting point to understand the essential function of B52 at the organismal level.