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Search Results: 1 - 10 of 7650 matches for " Taesung Park "
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Pathway-Based Evaluation in Early Onset Colorectal Cancer Suggests Focal Adhesion and Immunosuppression along with Epithelial-Mesenchymal Transition
Seungyoon Nam, Taesung Park
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0031685
Abstract: Colorectal cancer (CRC) has one of the highest incidences among all cancers. The majority of CRCs are sporadic cancers that occur in individuals without family histories of CRC or inherited mutations. Unfortunately, whole-genome expression studies of sporadic CRCs are limited. A recent study used microarray techniques to identify a predictor gene set indicative of susceptibility to early-onset CRC. However, the molecular mechanisms of the predictor gene set were not fully investigated in the previous study. To understand the functional roles of the predictor gene set, in the present study we applied a subpathway-based statistical model to the microarray data from the previous study and identified mechanisms that are reasonably associated with the predictor gene set. Interestingly, significant subpathways belonging to 2 KEGG pathways (focal adhesion; natural killer cell-mediated cytotoxicity) were found to be involved in the early-onset CRC patients. We also showed that the 2 pathways were functionally involved in the predictor gene set using a text-mining technique. Entry of a single member of the predictor gene set triggered a focal adhesion pathway, which confers anti-apoptosis in the early-onset CRC patients. Furthermore, intensive inspection of the predictor gene set in terms of the 2 pathways suggested that some entries of the predictor gene set were implicated in immunosuppression along with epithelial-mesenchymal transition (EMT) in the early-onset CRC patients. In addition, we compared our subpathway-based statistical model with a gene set-based statistical model, MIT Gene Set Enrichment Analysis (GSEA). Our method showed better performance than GSEA in the sense that our method was more consistent with a well-known cancer-related pathway set. Thus, the biological suggestion generated by our subpathway-based approach seems quite reasonable and warrants a further experimental study on early-onset CRC in terms of dedifferentiation or differentiation, which is underscored in EMT and immunosuppression.
Identification of differentially expressed subnetworks based on multivariate ANOVA
Taeyoung Hwang, Taesung Park
BMC Bioinformatics , 2009, DOI: 10.1186/1471-2105-10-128
Abstract: Given the MANOVA-based scoring method, we performed a greedy search to identify the subnetworks with the maximum scores in the PPI network. Our approach was successfully applied to human microarray datasets. Each identified subnetwork was annotated with the Gene Ontology (GO) term, resulting in the phenotype-related functional pathway or complex. We also compared these results with those of other scoring methods such as t statistic- and mutual information-based scoring methods. The MANOVA-based method produced subnetworks with a larger number of proteins than the other methods. Furthermore, the subnetworks identified by the MANOVA-based method tended to consist of highly correlated proteins.This article proposes a MANOVA-based scoring method to combine PPI data with expression data using a greedy search. This method is recommended for the highly sensitive detection of large subnetworks.Since the advent of microarray technology, genome-wide expression analysis has become an important tool in biology [1]. Much of the initial research with expression data has focused on evaluating the significance of individual genes in a comparison between two groups of samples to identify differentially expressed genes (DEGs). Various statistical approaches have been proposed in the literature [2-4]. However, the main difficulty lies not in the identification of DEGs but in their interpretation [5]. The interpretation of a long list of DEGs can be a daunting and ad hoc endeavor, since it is dependent on the biologist's area of expertise [6]. A compromise can be reached by constructing sets of individual genes (hereafter referred to as "gene sets") from prior biological data. For example, gene sets from a public database such as Gene Ontology (GO) and KEGG would provide functional interpretations of biological modules. In addition, by extending the level of analysis from an individual gene to multiple genes, we can identify genes with small changes that are not identified at the singl
Boolean networks using the chi-square test for inferring large-scale gene regulatory networks
Haseong Kim, Jae K Lee, Taesung Park
BMC Bioinformatics , 2007, DOI: 10.1186/1471-2105-8-37
Abstract: Both the computation time and accuracy of the network structures estimated by the proposed method are compared with those of the original BN methods on simulated and real yeast cell cycle microarray gene expression data sets. Our results reveal that the proposed chi-square testing (CST)-based BN method significantly improves the computation time, while its ability to identify all the true network mechanisms was effectively the same as that of full-search BN methods. The proposed BN algorithm is approximately 70.8 and 7.6 times faster than the original BN algorithm when the error sizes of the Best-Fit Extension problem are 0 and 1, respectively. Further, the false positive error rate of the proposed CST-based BN algorithm tends to be less than that of the original BN.The CST-based BN method dramatically improves the computation time of the original BN algorithm. Therefore, it can efficiently infer large-scale gene regulatory network mechanisms.The advancement of high-throughput technologies, such as DNA chips, has enabled the study of interactions and regulations among genes on a genome-wide scale. Recently, many algorithms have been introduced to determine gene regulatory networks based on such high-throughput microarray data, including linear models [1,2], Boolean networks [3-6], Bayesian networks [7,8], neural networks [9], and differential equations [1,10].In the linear modeling of a genetic network, the expression data is fitted using a regression model, where the change in expression levels is a response for all other genes [1]. Although such standard linear modeling approaches enable the analysis of many different features of the modeled system, they are not effective in genome-wide network discovery. This is because the number of candidate parameters and models is very high and therefore it is difficult to search efficiently and reliably with tight control on many false positives.Bayesian network algorithms have limitations with regard to the determination of
Exploratory Methods for Checking Quality of Microarray Data
Eun-Kyung Lee,Taesung Park
Bioinformation , 2007,
Abstract: In microarray experiments many undesirable systematic variations are commonly observed. Often investigators analyzing microarray data need to make subjective decisions about the quality of the experiment, by examining its chip image and a simple scatter plot. Thus, a more rigorous but simple method is desirable to determine the quality of microarray data. We propose two exploratory methods to investigate the quality of microarray experiments with replicated chips. The first method is based on correlations among chips and the second on the actual intensity values for each gene. The proposed methods are illustrated using a real microarray data set. The methods provide an initial estimation for determining the quality of microarray experiments.
A Modified Entropy-Based Approach for Identifying Gene-Gene Interactions in Case-Control Study
Jaeyong Yee, Min-Seok Kwon, Taesung Park, Mira Park
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0069321
Abstract: Gene-gene interactions may play an important role in the genetics of a complex disease. Detection and characterization of gene-gene interactions is a challenging issue that has stimulated the development of various statistical methods to address it. In this study, we introduce a method to measure gene interactions using entropy-based statistics from a contingency table of trait and genotype combinations. We also developed an exploration procedure by using graphs. We propose a standardized relative information gain (RIG) measure to evaluate the interactions between single nucleotide polymorphism (SNP) combinations. To identify the kth order interactions, contingency tables of trait and genotype combinations of k SNPs are constructed, with which RIGs are calculated. The RIGs are standardized using the mean and standard deviation from the permuted datasets. SNP combinations yielding high standardized RIG are chosen for gene-gene interactions. Detection of high-order interactions and comparison of interaction strengths between different orders are made possible by using standardized RIG. We have applied the proposed standardized entropy-based method to two types of data sets from a simulation study and a real genetic association study. We have compared our method and the multifactor dimensionality reduction (MDR) method through power analysis of eight different genetic models with varying penetrance rates, number of SNPs, and sample sizes. Our method shows successful identification of genetic associations and gene-gene interactions both in simulation and real genetic data. Simulation results suggest that the proposed entropy-based method is better able to detect high-order interactions and is superior to the MDR method in most cases. The proposed method is well suited for detecting interactions without main effects as well as for models including main effects.
Response projected clustering for direct association with physiological and clinical response data
Sung-Gon Yi, Taesung Park, Jae K Lee
BMC Bioinformatics , 2008, DOI: 10.1186/1471-2105-9-76
Abstract: We introduced a novel clustering analysis approach, response projected clustering (RPC), which uses a high-dimensional geometrical projection of response data to the gene expression space. The projected response vector, which becomes the origin in the projected space, is then clustered together with the projected gene vectors based on their different degrees of association with the response vector. A bootstrap-counting based RPC analysis is also performed to evaluate statistical tightness of identified gene clusters. Our RPC analysis was applied to the in vitro growth-inhibition and microarray profiling data on the NCI-60 cancer cell lines and the microarray gene expression study of macrophage differentiation in atherogenesis. These RPC applications enabled us to identify many known and novel gene factors and their potential pathway associations which are highly relevant to the drug's chemosensitivity activities and atherogenesis.We have shown that RPC can effectively discover gene networks with different degrees of association with clinical metadata. Performed on each gene's response projected vector based on its degree of association with the response data, RPC effectively summarizes individual genes' association with metadata as well as their own expression patterns. Thus, RPC greatly enhances the utility of clustering analysis on investigating high-dimensional microarray gene expression data with quantitative metadata.Microarray expression profiling has been widely applied to biological studies because of its ability to simultaneously examine tens of thousands of gene expression patterns. Microarray experiments have also proven to be quite useful for investigating associations between genes and physiological and clinical response measurements of many human diseases [1-3]. In particular, unsupervised learning techniques such as hierarchical clustering analysis have become one of the most commonly-used techniques for analyzing microarray data since these technique
Statistical Matching using Fractional Imputation
Jae-kwang Kim,Emily Berg,Taesung Park
Statistics , 2015,
Abstract: Statistical matching is a technique for integrating two or more data sets when information available for matching records for individual participants across data sets is incomplete. Statistical matching can be viewed as a missing data problem where a researcher wants to perform a joint analysis of variables that are never jointly observed. A conditional independence assumption is often used to create imputed data for statistical matching. We consider an alternative approach to statistical matching without using the conditional independence assumption. We apply parametric fractional imputation of Kim (2011) to create imputed data using an instrumental variable assumption to identify the joint distribution. We also present variance estimators appropriate for the imputation procedure. We explain how the method applies directly to the analysis of data from split questionnaire designs and measurement error models.
Microfluidic Technologies for Synthetic Biology
Parisutham Vinuselvi,Seongyong Park,Minseok Kim,Jung Min Park,Taesung Kim,Sung Kuk Lee
International Journal of Molecular Sciences , 2011, DOI: 10.3390/ijms12063576
Abstract: Microfluidic technologies have shown powerful abilities for reducing cost, time, and labor, and at the same time, for increasing accuracy, throughput, and performance in the analysis of biological and biochemical samples compared with the conventional, macroscale instruments. Synthetic biology is an emerging field of biology and has drawn much attraction due to its potential to create novel, functional biological parts and systems for special purposes. Since it is believed that the development of synthetic biology can be accelerated through the use of microfluidic technology, in this review work we focus our discussion on the latest microfluidic technologies that can provide unprecedented means in synthetic biology for dynamic profiling of gene expression/regulation with high resolution, highly sensitive on-chip and off-chip detection of metabolites, and whole-cell analysis.
A set of stage-specific gene transcripts identified in EK stage X and HH stage 3 chick embryos
Bo Lee, Heebal Kim, Tae Park, Sunjin Moon, Seoae Cho, Taesung Park, Jeong Lim, Jae Han
BMC Developmental Biology , 2007, DOI: 10.1186/1471-213x-7-60
Abstract: Microarray analysis primarily showed 40 genes indicating the significant changes in expression levels between stage X and HH stage 3, and 80% of the genes (32/40) were differentially expressed with more than a twofold change. Among those, 72% (23/32) were relatively up-regulated at stage X compared to HH stage 3, while 28% (9/32) were relatively up-regulated at HH stage 3 compared to stage X. Verification and gene expression profiling of these GeneChip expression data were performed using quantitative RT-PCR for 32 genes at developmental four points; stage X (0 h), HH stage 3 (12 h), HH stage 6 (24 h), and HH stage 9 (30 h). Additionally, we further analyzed four genes with less than twofold expression increase at HH stage 3. As a result, we identified a set of stage-specific genes during the early chick embryo development; 21 genes were relatively up-regulated in the stage X embryo and 12 genes were relatively up-regulated in the HH stage 3 embryo based on both results of microarray and quantitative RT-PCR.We identified a set of genes with stage-specific expression from microarray Genechip and quantitative RT-PCR. Discovering stage-specific genes will aid in uncovering the molecular mechanisms involved the formation of the three germ layers and germ cell segregation in the early chick embryos.In embryogenesis, a series of developmental events begins immediately after fertilization. During the early embryo development, the expression of many genes is spatiotemporally triggered or suppressed, under tight transcriptional control. However, the intricate changes in gene expression in the early embryo have yet to be investigated in detail in mammals or birds.As avian species are oviparous, the embryo is readily accessible even at the earliest stages and can effectively be manipulated for purposes including profiling the genes expressed in embryogenesis. Recently, the chicken genome was completely sequenced [1], and furthermore, Burt et al [2] reported that the organizati
Multiple Pathway-Based Genetic Variations Associated with Tobacco Related Multiple Primary Neoplasms
Ashwin Kotnis, Junghyun Namkung, Sadhana Kannan, Nallala Jayakrupakar, Taesung Park, Rajiv Sarin, Rita Mulherkar
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0030013
Abstract: Background In order to elucidate a combination of genetic alterations that drive tobacco carcinogenesis we have explored a unique model system and analytical method for an unbiased qualitative and quantitative assessment of gene-gene and gene-environment interactions. The objective of this case control study was to assess genetic predisposition in a biologically enriched clinical model system of tobacco related cancers (TRC), occurring as Multiple Primary Neoplasms (MPN). Methods Genotyping of 21 candidate Single Nucleotide Polymorphisms (SNP) from major metabolic pathways was performed in a cohort of 151 MPN cases and 210 cancer-free controls. Statistical analysis using logistic regression and Multifactor Dimensionality Reduction (MDR) analysis was performed for studying higher order interactions among various SNPs and tobacco habit. Results Increased risk association was observed for patients with at least one TRC in the upper aero digestive tract (UADT) for variations in SULT1A1 Arg213His, mEH Tyr113His, hOGG1 Ser326Cys, XRCC1 Arg280His and BRCA2 Asn372His. Gene - environment interactions were assessed using MDR analysis. The overall best model by MDR was tobacco habit/p53(Arg/Arg)/XRCC1(Arg399His)/mEH(Tyr113His) that had highest Cross Validation Consistency (8.3) and test accuracy (0.69). This model also showed significant association using logistic regression analysis. Conclusion This is the first Indian study on a multipathway based approach to study genetic susceptibility to cancer in tobacco associated MPN. This approach could assist in planning additional studies for comprehensive understanding of tobacco carcinogenesis.
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