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IEEE International Conference on bioinformatics and Biomedicine

作者： Sungkyoung Choi Sungyoung Lee Iksoo Huh Heungsun Hwang Taesung Park Interdisciplinary Program in Bioinformatics Seoul National University Department of Statistics Seoul National University Department of Psychology McGill University

ISBN: (纸本)9781467368001

There is an increasing interest in the pathway analysis of multiple genes and complex traits in association studies. Recently, a number of methods of pathway analysis have been developed to detect the novel pathways associated with human complex traits. In this paper, we propose a novel statistical approach for competitive pathway analysis based on Structural Equation Modeling (CPA-SEM), taking advantage of prior knowledge on existing relationships between genes in a pathway. Our CPA-SEM identifies pathways associated with traits of interest. The CPA-SEM approach is different from the previous SEM-based approaches in that it considers all possible sub-pathways into account and performs permutation based robust analysis. We applied the proposed CPA-SEM method to gene expression data of gastric cancer (GSE27342), and found that mTOR signaling pathway was significantly associated with gastric cancer. This pathway has previously been reported to be associated with gastric cancer. In conclusion, our CPA-SEM analysis provides a better understanding of biological mechanism by identifying pathways associated with a trait of interest.

关键词： Pathway analysis Structural equation modeling Prior biological knowledge Competitive approach Permutation test

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Unbalanced sample size effect on the genome-wide population differentiation studies

Unbalanced sample size effect on the genome-wide population ...

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IEEE International Conference on bioinformatics and Biomedicine Workshops (BIBMW)

作者： Kyunghee Han Kyee-zu Kim Taesung Park Department of Statistics Seoul National University South Korea Interdisciplinary program in Bioinformatics Seoul National University South Korea

The fixation index (Fst) is one of the most widely used measurements of genetic distance between populations. The data set from the international HapMap project has been served as a reference data set for population differentiation studies. Fst is commonly used in order to compare the sample data with HapMap data. In this study, however, we show that Fst without consideration of sample sizes may mislead the result. In particular, we first demonstrate that Fst suffers from imbalance of sample sizes through simulation studies and through the analysis of a large scale Korean genome-wide association (GWA) data. Then, we propose a modified version of Fst which is shown to be more robust to imbalance of sample size. In addition, the chi-square test commonly used for homogeneity test is shown to perform similarly to the modified version of Fst.

关键词： bioinformatics Genomics Indexes Estimation Frequency estimation Correlation

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GWAS-GMDR: A program package for genome-wide scan of gene-gene interactions with covariate adjustment based on multifactor dimensionality reduction

GWAS-GMDR: A program package for genome-wide scan of gene-ge...

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IEEE International Conference on bioinformatics and Biomedicine Workshops (BIBMW)

作者： Min-Seok Kwon Kyunga Kim Sungyoung Lee Wonil Chung Sung-Gon Yi Junghyun Namkung Taesung Park Interdisciplinary program in Bioinformatics Seoul National University South Korea Department of Statistics Seoul National University South Korea

Multifactor dimensionality reduction (MDR) has been successfully applied to identification of gene-gene interactions for the complex traits. Generalized MDR (GMDR) was its extension that allows adjustment for covariates. The current GMDR software mainly focuses on candidate gene association studies with a relatively small number of genetic markers and has some limitations to be extended to genome-wide association studies (GWAS) with a large number of genetic markers. We develop GWAS-GMDR, an effective parallel computing program package with special features for GWAS with a large number of genetic markers by using distributed job scheduling method and/or CUDA-enabled high-performance graphic processing units (GPU). First, GWAS-GMDR implements an effective memory handling algorithm and efficient procedures for GMDR to make joint analysis of multiple genes feasible for GWAS. Second, a weighted version of cross-validation consistency based on `top-K selection' (WCVC K ) is proposed to report multiple candidates for causal gene-gene interactions. Third, various performance measures are implemented to evaluate MDR classifiers, including balanced accuracy, tau-b, likelihood ratio and normalized mutual information. Fourth, some popular methods for handling missing genotypes are implemented. Finally, our applications support both CPU-based and GPU-based parallel computing system. We applied our applications using a real genome wide data set from WTCCC Crohn's disease dataset to identify two-way interaction models in genome-wide scale. The GWAS-GMDR package is a powerful tool for the gene-gene interaction analysis in a genome-wide scale. High-performance implementations are provided as native binaries for Linux, Mac OS X and Windows systems.

关键词： bioinformatics Graphics processing unit Genomics Diseases Parallel processing

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GxG-Viztool: A program for visualizing gene-gene interactions in genetic association analysis

GxG-Viztool: A program for visualizing gene-gene interaction...

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IEEE International Conference on bioinformatics and Biomedicine Workshops (BIBMW)

作者： Kyunga Kim Min-Seok Kwon Sung Young Lee Junghyun Namkung Ming D. Li Taesung Park Department of Statistics Sookmyung Womens University Seoul South Korea Interdisciplinary Program in Bioinformatics Seoul National University Seoul South Korea Department of Psychiatry and Neurobehavioral Sciences University of Virginia Charlottesville VA USA Interdisciplinary Program in Bioinformatics Department of Statistics Seoul National University Seoul South Korea

ISBN: (纸本)9781467327466

Gene-gene interactions are important factors underlying a common complex trait that is mostly polygenic. While many methods have been proposed to analyze gene-gene interactions in genetic association studies, the interpretation of the identified gene-gene interactions is not straightforward. In order to aid the interpretation of gene-gene interactions, we developed the GxG-Viztool, an executable program for visualizing gene-gene interactions in genetic association analysis. The GxG-Viztool provides an effective way to recognize genotype combinations that enhance/repress a trait and to display polygenic structure of interactions. The GxG-Viztool implements six graphical tools: checkerboard, pairwise checkerboard, forest, funnel, 3D lattice and parallel coordinate plots, which make it effective to recognize certain patterns in gene-gene interactions. It is freely available at http ://bibs. snu. ac. kr/GxG-Viz tool.

关键词： Data visualization Genetics Lattices Visualization Educational institutions Pattern recognition Heating

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Inference of large-scale gene regulatory networks using regression-based network approach

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Journal of bioinformatics and Computational Biology 2009年第4期7卷 717-735页

作者： Kim, Haseong Lee, Jae K. Park, Taesung Interdisciplinary Program in Bioinformatics Seoul National University Seoul San 56-1 Shilim-dong South Korea Division of Biostatistics and Epidemiology University of Virginia Charlottesville VA 22908 United States Department of Statistics Seoul National University Seoul San 56-1 Shilim-dong South Korea

The gene regulatory network modeling plays a key role in search for relationships among genes. Many modeling approaches have been introduced to find the causal relationship between genes using time series microarray data. However, they have been suffering from high dimensionality, overfitting, and heavy computation time. Further, the selection of a best model among several possible competing models is not guaranteed that it is the best one. In this study, we propose a simple procedure for constructing large scale gene regulatory networks using a regression-based network approach. We determine the optimal out-degree of network structure by using the sum of squared coefficients which are obtained from all appropriate regression models. Through the simulated data, accuracy of estimation and robustness against noise are computed in order to compare with the vector autoregressive regression model. Our method shows high accuracy and robustness for inferring large-scale gene networks. Also it is applied to Caulobacter crecentus cell cycle data consisting of 1472 genes. It shows that many genes are regulated by two transcription factors, ctrA and gcrA, that are known for global regulators. © 2009 Imperial College Press.

关键词： Large-scale gene regulatory networks Linear regression Scale-free networks Weighted voting

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Gene-gene interaction analysis for the survival phenotype based on the standardized residuals from parametric regression models

Gene-gene interaction analysis for the survival phenotype ba...

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IEEE International Conference on bioinformatics and Biomedicine Workshops (BIBMW)

作者： Seungyeoun Lee Jinseok Oh Min-Seok Kwon Taesung Park Department of Mathematics and Statistics Sejong University South Korea Interdisciplinary Program in Bioinformatics Seoul National University Seoul South Korea

Most of statistical methods in genome-wide association studies (GWAS) have been developed to identify SNP-SNP interactions using a binary phenotype for case-control studies. However, there is an interest in identifying SNPs and combinations of SNPs that relate to the survival phenotype in many cancer studies. Recently, Gui et al. (2011) proposed a novel method, called Surv-MDR, for detecting gene-gene interactions associated with survival time by modifying the multifactor dimensionality reduction (MDR) method based on the log-rank test. However, the Surv-MDR method needs more-intensive computations and does not allow for a covariate adjustment. Lee et al. (2011) proposed an alternative approach, called Cox-MDR, for detecting gene-gene interactions based on a Cox model by extending generalized multifactor dimensionality reduction (GMDR) to the survival time phenotype. The main idea of Cox-MDR is to use a martingale residual as a score to classify multi-level genotypes as high and low risk groups. Cox-MDR also allows the effects of discrete and quantitative covariates to be easily adjusted in the Cox model and requires much less computation than Surv-MDR. In this paper, we propose to extend the idea of a Cox-MDR to the parametric regression model, called PRM-MDR for identifying gene-gene interactions associated with the survival phenotype. The main idea of PRM-MDR is to use a standardized residual from a parametric regression model as a score to classify multi-level genotypes as high and low risk groups while keeping the rest of the MDR procedure unchanged. We performed a simulation study to compare PRM-MDR with Cox-MDR as well as the Surv-MDR method in detecting two-way interactions. The simulation setting is constructed as similar as that of Gui et al. (2011), so that three methods are comparable. In addition, we compared the power of PRM-MDR with adjusting the covariates versus without adjusting the covariates in two different scenarios. In the first scenario, we a

关键词： Accuracy Computational modeling Cancer Testing Analytical models Mathematical model Training

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cuGWAM: Genome-wide association multifactor dimensionality reduction using CUDA-enabled high-performance graphics processing unit

cuGWAM: Genome-wide association multifactor dimensionality r...

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IEEE International Conference on bioinformatics and Biomedicine Workshops (BIBMW)

作者： Min-Seok Kwon Kyunga Kim Sungyoung Lee Taesung Park Interdisciplinary program in Bioinformatics Seoul National University South Korea Department of Statistics Seoul National University South Korea Department of statistics Seoul National University South Korea

Multifactor dimensionality reduction (MDR) method has been widely applied to detect gene-gene interactions that are well recognized as playing an important role in understanding complex traits, such as disease susceptibility. However, because of an exhaustive analysis of MDR, the current MDR software has some limitations to be extended to the genome-wide association studies (GWAS) with a large number of genetic markers up to ~1 million. To overcome this computational problem, we developed CUDA based genome-wide association MDR (cuGWAM) software using efficient hardware accelerators. Not only cuGWAM has better performance than CPU-based MDR methods (original MDR and parallel MDR) and GPU-based other methods (MDRGPU), but also initial construction cost is also less expensive. Furthermore, cuGWAM provides various performance measures to evaluate MDR classifiers, including tau-b, likelihood ratio, normalized mutual information as well as balanced accuracy. Also, cuGWAM provided three methods for handling missing genotypes: complete, available and missing category. Executable cuGWAM are freely available at http://***/cugwam for system with CUDA-enabled GPU devices.

关键词： Graphics processing unit bioinformatics Genomics Performance evaluation Classification algorithms Graphics Instruction sets

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Confidence Interval Estimation for Machine Learning Models in Forecasting Infectious Diseases

Confidence Interval Estimation for Machine Learning Models i...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Taewan Goo Kyulhee Han Hanbyul Song Jiwon Park Zhe Liu Jooha Oh Sayooj Aby Jose Taesung Park Interdisciplinary Program in Bioinformatics Seoul National University Seoul Republic of Korea Department of Statistics Seoul National University Seoul Republic of Korea

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Forecasting models have been instrumental in managing the COVID-19 pandemic by facilitating efficient resource distribution and proper interventions. Although various forecasting models—such as mathematical models, statistical models, and machine learning models—are available, a few models account for the uncertainty in their predictions. The lack of uncertainty information constrains the reliability of forecasting results, making them less applicable for use in decision-making. Confidence Intervals (CIs) are widely used in statistical inference to provide uncertainty information. In this study, we introduce a framework that provides bootstrap-based CIs readily applicable to various forecasting models. The key concept of this framework is repeated training on bootstrap data based on resampled residuals from the forecasting model. After fitting the forecasting model, bootstrap datasets are generated considering time series characteristics to retrain the forecasting models. Then, CIs are calculated based on the distribution of prediction values. This framework was applied to forecasting COVID-19 outcomes—such as daily confirmed COVID-19 cases, deaths, and ICU patients—in South Korea. Our results demonstrate that bootstrap-based CIs can be successfully applied to provide additional information on the reliability of prediction. In conclusion, our framework can provide uncertainty information without requiring complex assumptions, regardless of the forecasting model or response variable type. Therefore, this approach can support public health policymakers in gaining a deeper understanding of epidemic trends, thereby offering valuable insights for decision-making.

关键词： COVID-19 Training Uncertainty Biological system modeling Decision making Machine learning Predictive models Mathematical models Reliability Forecasting

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CUDA-LR: CUDA-accelerated logistic regression analysis tool for gene-gene interaction for genome-wide association study

CUDA-LR: CUDA-accelerated logistic regression analysis tool ...

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IEEE International Conference on bioinformatics and Biomedicine Workshops (BIBMW)

作者： Sungyoung Lee Min-Seok Kwon Ik-Soo Huh Taesung Park Interdisciplinary Program in Bioinformatics Seoul National University Seoul South Korea Department of Statistics Seoul National University Seoul South Korea

In genome-wide association studies (GWAS), logistic regression (LR) has been most commonly used for finding an association between a disease phenotype and genetic variants such as single nucleotide polymorphism (SNP). Since logistic regression model requires iterative algorithms to get the parameter estimates, its application to GWAS has been limited to the identification of the individual SNPs. Thus, there have been limited applications of LR to multiple SNP analysis including gene-gene interaction analysis in large scale GWAS data. To overcome this computational burden, we developed a logistic regression analysis tool named CUDA-LR, based on the new programming architecture using Graphics Processing Unit (GPU). CUDA-LR supports not only the simple model with single SNP but also more complex model with two SNPs including the interaction. In addition, CUDA-LR provides various parameters to gain more acceleration and perform specified analysis. In the comparison between our analysis and the other methods, CUDA-LR showed almost 700-folds of acceleration and highly reliable results by our GPU specified optimization techniques. We believe that the CUDA-LR now is a useful logistic regression analysis tool for interaction analysis of large scale GWAS datasets.

关键词： Graphics processing unit bioinformatics Acceleration Genomics Logistics Mathematical model programming

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Pathway based identification of rare mutation effect in cancer

Pathway based identification of rare mutation effect in canc...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： TaeJin Ahn Taesung Park Interdisciplinary Program in Bioinformatics Seoul National University Seoul Republic of Korea Department of statistics Seoul National University Seoul Republic of Korea

ISBN: (纸本)9781479913114

Identifying driver mutation is important in understanding disease mechanism and future application of custom tailored therapeutic decision. Functional analysis of mutational impact usually focuses on the gene expression level of the mutated gene. However, complex regulatory network may cause differential gene expression among functional neighbors of the mutated gene. We suggest a new approach for discovering rare mutations that have real impact in the context of pathway; philosophy of our method is iteratively combining rare mutations until no more mutations can be added, under the condition that the combined mutational event can statistically discriminate pathway level mRNA expression between groups with and without mutation events. Our approach is shown to sensitively capture mutations that change pathway level gene expression at breast cancer data.

关键词： Gene expression Breast cancer Drugs Tumors Proteins

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