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Search Results: 1 - 10 of 25887 matches for " Seon-Young Kim "
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Effects of sample size on robustness and prediction accuracy of a prognostic gene signature
Seon-Young Kim
BMC Bioinformatics , 2009, DOI: 10.1186/1471-2105-10-147
Abstract: A data set of 1,372 samples was generated by combining eight breast cancer gene expression data sets produced using the same microarray platform and, using the data set, effects of varying samples sizes on a few performances of a prognostic gene signature were investigated. The overlap between independently developed gene signatures was increased linearly with more samples, attaining an average overlap of 16.56% with 600 samples. The concordance between predicted outcomes by different gene signatures also was increased with more samples up to 94.61% with 300 samples. The accuracy of outcome prediction also increased with more samples. Finally, analysis using only Estrogen Receptor-positive (ER+) patients attained higher prediction accuracy than using both patients, suggesting that sub-type specific analysis can lead to the development of better prognostic gene signaturesIncreasing sample sizes generated a gene signature with better stability, better concordance in outcome prediction, and better prediction accuracy. However, the degree of performance improvement by the increased sample size was different between the degree of overlap and the degree of concordance in outcome prediction, suggesting that the sample size required for a study should be determined according to the specific aims of the study.Recent advances in various high-throughput technologies including genome sequencing, transcriptomics, genome-wide SNP analysis, proteomics, glycomics, and metabolomics have opened up new opportunities for developing prognostic and predictive markers for better treatment of diverse diseases. Indeed, many researchers have reported promising results for improved patient treatment by providing more accurate prognostic and predictive information for decision making [1-3]. Among various high-throughput technologies, microarray gene expression profiling has been widely used for prognostic and predictive marker development for its rich information. The use of gene expression pr
Genome-wide prediction of transcriptional regulatory elements of human promoters using gene expression and promoter analysis data
Seon-Young Kim, YongSung Kim
BMC Bioinformatics , 2006, DOI: 10.1186/1471-2105-7-330
Abstract: We report herein a new method that combines gene expression data analysis with promoter analysis to infer transcriptional regulatory elements of human genes. The Z scores from the application of gene set analysis with gene sets of transcription factor binding sites (TFBSs) were successfully used to represent the activity of TFBSs in a given microarray data set. A significant correlation between the Z scores of gene sets of TFBSs and individual genes across multiple conditions permitted successful identification of many known human transcriptional regulatory elements of genes as well as the prediction of numerous putative TFBSs of many genes which will constitute a good starting point for further experiments. Using Z scores of gene sets of TFBSs produced better predictions than the use of mRNA levels of a transcription factor itself, suggesting that the Z scores of gene sets of TFBSs better represent diverse mechanisms for changing the activity of transcription factors in the cell. In addition, cis-regulatory modules, combinations of co-acting TFBSs, were readily identified by our analysis.By a strategic combination of gene set level analysis of gene expression data sets and promoter analysis, we were able to identify and predict many transcriptional regulatory elements of human genes. We conclude that this approach will aid in decoding some of the important transcriptional regulatory elements of human genes.With the genome sequences of many organisms completed, revealing the regulatory mechanisms of gene expression is the important aspect of genomics [1]. Recent innovative technologies such as microarray and chromatin immunoprecipitation combined with chip (ChIP – CHIP), and the whole genome sequencing of many organisms are producing enormous amounts of data that are useful in elucidating the transcriptional regulatory mechanisms of genes. Whole genome sequences provide information on the cis-acting regulatory elements of each gene. Gene expression data provide info
A gene sets approach for identifying prognostic gene signatures for outcome prediction
Seon-Young Kim, Yong Sung Kim
BMC Genomics , 2008, DOI: 10.1186/1471-2164-9-177
Abstract: We applied a gene sets approach to develop a prognostic gene set from multiple gene expression datasets. By analyzing 12 independent breast cancer gene expression datasets comprising 1,756 tissues with 2,411 pre-defined gene sets including gene ontology categories and pathways, we found many gene sets that were prognostic in most of the analyzed datasets. Those prognostic gene sets were related to biological processes such as cell cycle and proliferation and had additional prognostic values over conventional clinical parameters such as tumor grade, lymph node status, estrogen receptor (ER) status, and tumor size. We then estimated the prediction accuracy of each gene set by performing external validation using six large datasets and identified a gene set with an average prediction accuracy of 67.55%.A gene sets approach is an effective method to develop prognostic gene sets to predict patient outcome and to understand the underlying biology of the developed gene set. Using the gene sets approach we identified many prognostic gene sets in breast cancer.Many researchers have studied the feasibility of gene expression profiling to improve the prognosis of cancer patients and have shown that gene expression signatures can better predict the outcome of cancer patients than conventional clinical criteria in many cancer types [1-4]. A few of the discovered signatures are now in large clinical trials to confirm their prognostic value [5,6]. However, there are also concerns about the usefulness of the gene expression signatures because several problems remain unresolved [7-9]. These problems include poor overlap among discovered gene signatures, the unstable nature of gene expression signatures, and poor performance of signatures when applied to other datasets [7,9-11].Researchers have applied either top-down or bottom-up approaches to discover prognostic gene signatures [12]. Most researchers have used the top-down approach in which samples are split into training and testi
PAGE: Parametric Analysis of Gene Set Enrichment
Seon-Young Kim, David J Volsky
BMC Bioinformatics , 2005, DOI: 10.1186/1471-2105-6-144
Abstract: We developed a modified gene set enrichment analysis method based on a parametric statistical analysis model. Compared with GSEA, the parametric analysis of gene set enrichment (PAGE) detected a larger number of significantly altered gene sets and their p-values were lower than the corresponding p-values calculated by GSEA. Because PAGE uses normal distribution for statistical inference, it requires less computation than GSEA, which needs repeated computation of the permutated data set. PAGE was able to detect significantly changed gene sets from microarray data irrespective of different Affymetrix probe level analysis methods or different microarray platforms. Comparison of two aged muscle microarray data sets at gene set level using PAGE revealed common biological themes better than comparison at individual gene level.PAGE was statistically more sensitive and required much less computational effort than GSEA, it could identify significantly changed biological themes from microarray data irrespective of analysis methods or microarray platforms, and it was useful in comparison of multiple microarray data sets. We offer PAGE as a useful microarray analysis method.High-throughput technologies such as DNA microarrays and proteomics are revolutionizing biology and medicine. Global gene expression profiling using microarrays monitors changes in expression of thousands of genes simultaneously. At the data acquisition level, gene expression profiles from a given system should be reproducible and yield statistically significant changes in gene expression [1]. The large amounts of data acquired must then be reduced or "translated" to a smaller set of genes representing meaningful biological differences between control and test systems and validated in an experimental or clinical setting [2]. Since inception of the microarray technology, significant technological and analytical improvements have been introduced to meet these challenges, from experimental design [1], probe-lev
Dynamic Motifs of Strategies in Prisoner's Dilemma Games
Young Jin Kim,Myungkyoon Roh,Seon-Young Jeong,Seung-Woo Son
Physics , 2014, DOI: 10.3938/jkps.65.1709
Abstract: We investigate the win-lose relations between strategies of iterated prisoner's dilemma games by using a directed network concept to display the replicator dynamics results. In the giant strongly-connected component of the win/lose network, we find win-lose circulations similar to rock-paper-scissors and analyze the fixed point and its stability. Applying the network motif concept, we introduce dynamic motifs, which describe the population dynamics relations among the three strategies. Through exact enumeration, we find 22 dynamic motifs and display their phase portraits. Visualization using directed networks and motif analysis is a useful method to make complex dynamic behavior simple in order to understand it more intuitively. Dynamic motifs can be building blocks for dynamic behavior among strategies when they are applied to other types of games.
MULTI-K: accurate classification of microarray subtypes using ensemble k-means clustering
Eun-Youn Kim, Seon-Young Kim, Daniel Ashlock, Dougu Nam
BMC Bioinformatics , 2009, DOI: 10.1186/1471-2105-10-260
Abstract: We present a cluster-number-based ensemble clustering algorithm, called MULTI-K, for microarray sample classification, which demonstrates remarkable accuracy. The method amalgamates multiple k-means runs by varying the number of clusters and identifies clusters that manifest the most robust co-memberships of elements. In addition to the original algorithm, we newly devised the entropy-plot to control the separation of singletons or small clusters. MULTI-K, unlike the simple k-means or other widely used methods, was able to capture clusters with complex and high-dimensional structures accurately. MULTI-K outperformed other methods including a recently developed ensemble clustering algorithm in tests with five simulated and eight real gene-expression data sets.The geometric complexity of clusters should be taken into account for accurate classification of microarray data, and ensemble clustering applied to the number of clusters tackles the problem very well. The C++ code and the data sets tested are available from the authors.Groups that exhibit similar patterns in large-scale genomic data sets have provided primary biological information. In this regard, identification of natural clusters and their membership has excited a great deal of interest in functional genomics and clinical research. Indeed, unsupervised clustering methods applied to microarray data enabled predictions of unknown gene functions (if applied to genes) [1,2] and suggested the existence of subtypes of disease (if applied to samples) [3-6]. The task of cluster identification heavily depends on the property of clusters that are of interest, e.g., compactness, connectedness, and spatial separation. Each clustering algorithm has pros and cons for different shapes of clusters, which in turn informs the choice of an appropriate clustering strategy [7].We are interested in establishing subclasses among microarray samples that might enable specified clinical treatments. In this problem, the data points a
GENT: Gene Expression Database of Normal and Tumor Tissues
Gwangsik Shin, Tae-Wook Kang, Sungjin Yang, Su-Jin Baek, Yong-Su Jeong and Seon-Young Kim
Cancer Informatics , 2012, DOI: 10.4137/CIN.S7226
Abstract: Background: Some oncogenes such as ERBB2 and EGFR are over-expressed in only a subset of patients. Cancer outlier profile analysis is one of computational approaches to identify outliers in gene expression data. A database with a large sample size would be a great advantage when searching for genes over-expressed in only a subset of patients. Description: GENT (Gene Expression database of Normal and Tumor tissues) is a web-accessible database that provides gene expression patterns across diverse human cancer and normal tissues. More than 40000 samples, profiled by Affymetrix U133A or U133plus2 platforms in many different laboratories across the world, were collected from public resources and combined into two large data sets, helping the identification of cancer outliers that are over-expressed in only a subset of patients. Gene expression patterns in nearly 1000 human cancer cell lines are also provided. In each tissue, users can retrieve gene expression patterns classified by more detailed clinical information. Conclusions: The large samples size (.24300 for U133plus2 and .16400 for U133A) of GENT provides an advantage in identifying cancer outliers. A cancer cell line gene expression database is useful for target validation by in vitro experiment. We hope GENT will be a useful resource for cancer researchers in many stages from target discovery to target validation. GENT is available at http://medical genome.kribb.re.kr/GENT/ or http://genome.kobic.re.kr/GENT/.
Significant Effects of Antiretroviral Therapy on Global Gene Expression in Brain Tissues of Patients with HIV-1-Associated Neurocognitive Disorders
Alejandra Borjabad,Susan Morgello,Wei Chao,Seon-Young Kim,Andrew I. Brooks,Jacinta Murray,Mary Jane Potash,David J. Volsky
PLOS Pathogens , 2011, DOI: 10.1371/journal.ppat.1002213
Abstract: Antiretroviral therapy (ART) has reduced morbidity and mortality in HIV-1 infection; however HIV-1-associated neurocognitive disorders (HAND) persist despite treatment. The reasons for the limited efficacy of ART in the brain are unknown. Here we used functional genomics to determine ART effectiveness in the brain and to identify molecular signatures of HAND under ART. We performed genome-wide microarray analysis using Affymetrix U133 Plus 2.0 Arrays, real-time PCR, and immunohistochemistry in brain tissues from seven treated and eight untreated HAND patients and six uninfected controls. We also determined brain virus burdens by real-time PCR. Treated and untreated HAND brains had distinct gene expression profiles with ART transcriptomes clustering with HIV-1-negative controls. The molecular disease profile of untreated HAND showed dysregulated expression of 1470 genes at p<0.05, with activation of antiviral and immune responses and suppression of synaptic transmission and neurogenesis. The overall brain transcriptome changes in these patients were independent of histological manifestation of HIV-1 encephalitis and brain virus burdens. Depending on treatment compliance, brain transcriptomes from patients on ART had 83% to 93% fewer dysregulated genes and significantly lower dysregulation of biological pathways compared to untreated patients, with particular improvement indicated for nervous system functions. However a core of about 100 genes remained similarly dysregulated in both treated and untreated patient brain tissues. These genes participate in adaptive immune responses, and in interferon, cell cycle, and myelin pathways. Fluctuations of cellular gene expression in the brain correlated in Pearson's formula analysis with plasma but not brain virus burden. Our results define for the first time an aberrant genome-wide brain transcriptome of untreated HAND and they suggest that antiretroviral treatment can be broadly effective in reducing pathophysiological changes in the brain associated with HAND. Aberrantly expressed transcripts common to untreated and treated HAND may contribute to neurocognitive changes defying ART.
LAP2 Is Widely Overexpressed in Diverse Digestive Tract Cancers and Regulates Motility of Cancer Cells
Hyun-Jung Kim, Sun-Hwi Hwang, Myoung-Eun Han, Sungmin Baek, Hey-Eun Sim, Sik Yoon, Sun-Yong Baek, Bong-Seon Kim, Jeong-Hwan Kim, Seon-Young Kim, Sae-Ock Oh
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0039482
Abstract: Background Lamina-associated polypeptides 2 (LAP2) is a nuclear protein that connects the nuclear lamina with chromatin. Although its critical roles in genetic disorders and hematopoietic malignancies have been described, its expression and roles in digestive tract cancers have been poorly characterized. Methods To examine the expression of LAP2 in patient tissues, we performed immunohistochemistry and real-time PCR. To examine motility of cancer cells, we employed Boyden chamber, wound healing and Matrigel invasion assays. To reveal its roles in metastasis in vivo, we used a liver metastasis xenograft model. To investigate the underlying mechanism, a cDNA microarray was conducted. Results Immunohistochemistry in patient tissues showed widespread expression of LAP2 in diverse digestive tract cancers including stomach, pancreas, liver, and bile duct cancers. Real-time PCR confirmed that LAP2β is over-expressed in gastric cancer tissues. Knockdown of LAP2β did not affect proliferation of most digestive tract cancer cells except pancreatic cancer cells. However, knockdown of LAP2β decreased motility of all tested cancer cells. Moreover, overexpression of LAP2β increased motility of gastric and pancreatic cancer cells. In the liver metastasis xenograft model, LAP2β increased metastatic efficacy of gastric cancer cells and mortality in tested mice. cDNA microarrays showed the possibility that myristoylated alanine-rich C kinase substrate (MARCKS) and interleukin6 (IL6) may mediate LAP2β-regulated motility of cancer cells. Conclusions From the above results, we conclude that LAP2 is widely overexpressed in diverse digestive tract cancers and LAP2β regulates motility of cancer cells and suggest that LAP2β may have utility for diagnostics and therapeutics in digestive tract cancers.
Copy number variations (CNVs) identified in Korean individuals
Tae-Wook Kang, Yeo-Jin Jeon, Eunsu Jang, Hee-Jin Kim, Jeong-Hwan Kim, Jong-Lyul Park, Siwoo Lee, Yong Kim, Jong Kim, Seon-Young Kim
BMC Genomics , 2008, DOI: 10.1186/1471-2164-9-492
Abstract: We identified 65 copy number variation regions (CNVRs) in 116 normal Korean individuals by analyzing Affymetrix 250 K Nsp whole-genome SNP data. Ten of these CNVRs were novel and not present in the Database of Genomic Variants (DGV). To increase the specificity of CNV detection, three algorithms, CNAG, dChip and GEMCA, were applied to the data set, and only those regions recognized at least by two algorithms were identified as CNVs. Most CNVRs identified in the Korean population were rare (<1%), occurring just once among the 116 individuals. When CNVs from the Korean population were compared with CNVs from the three HapMap ethnic groups, African, European, and Asian; our Korean population showed the highest degree of overlap with the Asian population, as expected. However, the overlap was less than 40%, implying that more CNVs remain to be discovered from the Asian population as well as from other populations. Genes in the novel CNVRs from the Korean population were enriched for genes involved in regulation and development processes.CNVs are recently-recognized structural variations among individuals, and more CNVs need to be identified from diverse populations. Until now, CNVs from Asian populations have been studied less than those from European or American populations. In this regard, our study of CNVs from the Korean population will contribute to the full cataloguing of structural variation among diverse human populations.Understanding variations in the human genome is the key to unraveling the phenotypic diversity among individuals and understanding various human diseases. Genomic variations exist at various levels, from differences in single nucleotides to microscopic chromosome-level variation [1]. Copy number variations (CNVs), a new type of genomic variation that has recently received considerable attention, are deletions, insertions, duplications, and more complex variations ranging from 1 kb to submicroscopic sizes [1-4]. Recent advances in array technolo
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