• Genomics & Informatics
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• v.20 no.4
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• pp.49.1-49.7
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• 2022

Many packages for a meta-analysis of genome-wide association studies (GWAS) have been developed to discover genetic variants. Although variations across studies must be considered, there are not many currently-accessible packages that estimate between-study heterogeneity. Thus, we propose a python based application called Beta-Meta which can easily process a meta-analysis by automatically selecting between a fixed effects and a random effects model based on heterogeneity. Beta-Meta implements flexible input data manipulation to allow multiple meta-analyses of different genotype-phenotype associations in a single process. It provides a step-by-step meta-analysis of GWAS for each association in the following order: heterogeneity test, two different calculations of an effect size and a p-value based on heterogeneity, and the Benjamini-Hochberg p-value adjustment. These methods enable users to validate the results of individual studies with greater statistical power and better estimation precision. We elaborate on these and illustrate them with examples from several studies of infertility-related disorders.

• Industrial Engineering and Management Systems
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• v.11 no.1
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• pp.114-122
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• 2012

This paper considers a non-identical parallel machine scheduling problem with sequence and machine dependent setup times. The objective of this problem is to determine the allocation of jobs and the scheduling of each machine to minimize makespan. A mathematical model for optimal solution is derived. An in-depth analysis of the model shows that it is very complicated and difficult to obtain optimal solutions as the problem size becomes large. Therefore, two meta-heuristics, genetic algorithm (GA) and a new population-based evolutionary meta-heuristic called self-evolution algorithm (SEA), are proposed. The performances of the meta-heuristic algorithms are evaluated through compare with optimal solutions using randomly generated several examples.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.23
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• pp.10175-10179
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• 2015

Background: The association between the RTEL1 rs6010620 single nucleotide polymorphism (SNP) and glioma risk has been extensively studied. However, the results remain inconclusive. To further examine this association, we performed a meta-analysis. Materials and Methods: A computerized search of the PubMed and Embase databases for publications regarding the RTEL1 rs6010620 polymorphism and glioma cancer risk was performed. Genotype data were analyzed in a meta-analysis. Odds ratios (ORs) with 95% confidence intervals (CIs) were estimated to assess the association. Sensitivity analyses, tests of heterogeneity, cumulative meta-analyses, and assessments of bias were performed in our meta-analysis. Results: Our meta-analysis confirmed that risk with allele A is lower than with allele G for glioma. The A allele of rs6010620 in RTEL1 decreased the risk of developing glioma in the 12 case-control studies for all genetic models: the allele model (OR=0.752, 95%CI: 0.715-0.792), the dominant model (OR=0.729, 95%CI: 0.685-0.776), the recessive model (OR=0.647, 95%CI: 0.569-0.734), the homozygote comparison (OR=0.528, 95%CI: 0.456-0.612), and the heterozygote comparison (OR=0.761, 95%CI: 0.713-0.812). Conclusions: In all genetic models, the association between the RTEL1 rs6010620 polymorphism and glioma risk was significant. This meta-analysis suggests that the RTEL1 rs6010620 polymorphism may be a risk factor for glioma. Further functional studies evaluating this polymorphism and glioma risk are warranted.

• Journal of Technology Innovation
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• v.15 no.1
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• pp.87-113
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• 2007

For the realization of the national self-defense, the trend of the defense R&D projects becomes global and diverse with the increase of their budget. Thus, for the effective execution of the budget and the successful performance of the projects, the importance of selection, management and evaluation of the R&D projects is emphasized. This study suggests the applicability of a meta-evaluation model for the realization of the successful defense R&D project evaluation system. This study focused on the development of the meta-evaluation model design which can be applicable to the national defense R&D projects. Using the proposed meta-evaluation model, we propose a technique to enhance the fairness and the reliability of the national defense R&D projects evaluation system.

• The Korean Journal of Applied Statistics
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• v.14 no.1
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• pp.161-175
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• 2001

Meta-analysis refers to quantitative methods for combining results from independent studies in order to draw overall conclusions. Hierarchical models including selection models are introduced and shown to be useful in such Bayesian meta-analysis. Semiparametric hierarchical models are proposed using the Dirichlet process prior. These rich class of models combine the information of independent studies, allowing investigation of variability both between and within studies, and weight function. Here we investigate sensitivity of results to unobserved studies by considering a hierachical selection model with including unknown weight function and use Markov chain Monte Carlo methods to develop inference for the parameters of interest. Using Bayesian method, this model is used on a meta-analysis of twelve studies comparing the effectiveness of two different types of flouride, in preventing cavities. Clinical informative prior is assumed. Summaries and plots of model parameters are analyzed to address questions of interest.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.57.3-57
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• 2002

1. Introduction 2. General flux balance model 3. MetaFluxNet 3.1 Overview of MetaFluxNet 3.2 Project file format 3.3 Construction of metabolite reaction model 3.4 Metabolic flux analysis using linear programming 3.5 Visualization of MFA results 4. Conclusion and plan 5. Acknowledgement. References.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.375-378
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• 2002

In this paper we propose parallel GA to optimize mutation rate and crossover rate using server-client model. The performance of GA depend on the good choice of crossover and mutation rates. Although many researcher has been study about the good choice, it is still unsolved problem. proposed GA optimize crossover and mutation rates trough evolving subpopulation. In virtue of the server-client model, these parameters can be evolved rapidly with relatively low-grade

• Journal of Korean Society for Quality Management
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• v.40 no.1
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• pp.49-59
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• 2012

In computer simulation models the output from the computer code is often deterministic, i.e., running the code twice with the same values for the input variables would give the same output. It is discussed why the response surface method with polynomial approximation for the true response function is a good approximation to the computer experiments model. A sequential strategy to find the proper reduced quadratic polynomial model is illustrated with a case study in the military war game computer simulation model.

• Journal of Distribution Science
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• v.1 no.1
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• pp.115-138
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• 1999

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.1
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• pp.111-115
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• 2012

Variants of X-ray repair cross-complementing group 1 (XRCC1) are involved in the development of cancer, but studies investigating the association of XRCC1-77T>C polymorphism with cancer risk have reported conflicting results. To clarify the effect of the XRCC1 -77T>C polymorphism on cancer risk, we performed a meta-analysis by conducting searches of the published literature in PubMed, Embase and CBM databases. Finally, 13 studies were included into our meta-analysis, involving a total of 11, 678 individuals. Subgroup analyses were performed by ethnicity and cancer type. The results of this meta-analysis showed that there was significant association between the C variant of XRCC1-77T>C polymorphism and cancer risk in all four genetic comparison models (ORC vs. T =1.19, 95%CI 1.07-1.31, P = 0.001; OR homozygote model =1.28, 95%CI 1.07-1.52, P = 0.007; OR recessive genetic model =1.22, 95%CI 1.04-1.44, P = 0.015; OR dominant model =1.21, 95% CI 1.07-1.35, P = 0.001). In the subgroup analyses based on ethnicity, the association was still significant in the Asian population (all p values<0.001), but not in the Caucasian population (all p values > 0.05). Thus, the XRCC1 -77T>C polymorphism is associated with cancer risk, and individuals with XRCC1 -77C variant have a significantly higher cancer risk, particularly in the Asian population.