• Asian Pacific Journal of Cancer Prevention
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• v.13 no.8
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• pp.3717-3721
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• 2012

Purpose: Any association between the CYP1A1 Ile462Val polymorphism and endometrial cancer risk remains inconclusive. For a more precise estimate, we performed the present meta-analysis. Methods: PUBMED, OVID and EMBASE were searched for the studies which met inclusion criteria. Data in all eligible studies were evaluated and extracted by two authors independently. The meta-analysis estimated pooled odds ratio (OR) with 95% confidence interval (CI) for endometrial cancer risk attributable to the CYP1A1 Ile462Val polymorphism. Results: A total of 7 studies were included in this meta-analysis. The results indicated no association between endometrial cancer risk and the CYP1A1 Ile462Val polymorphism (for Val vs Ile allele model [OR 1.09, 95% CI 0.73-1.62]; for Val.Val vs Ile.Ile genotype model [OR 1.54, 95% CI 0.56-4.23]; for (Ile.Val + Val.Val) vs Ile.Ile genotpye model [OR 1.08, 95% CI 0.71-1.63]; for Val.Val vs (Ile.Ile + Ile.Val) genotype model [OR 1.46, 95% CI 0.53-4.04]). Conclusions: This meta-analysis suggests that there is no association between endometrial cancer risk and the CYP1A1 Ile462Val polymorphism.

• Molecules and Cells
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• v.21 no.2
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• pp.269-275
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• 2006

A recent report of the Korean Intellectual Property Office(KIPO) showed that the number of biological sequence-based patents is rapidly increasing in Korea. We present biological features of Korean patented sequences though bioinformatic analysis. The analysis is divided into two steps. The first is an annotation step in which the patented sequences were annotated with the Reference Sequence (RefSeq) database. The second is an association step in which the patented sequences were linked to genes, diseases, pathway, and biological functions. We used Entrez Gene, Online Mendelian Inheritance in Man (OMIM), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Ontology (GO) databases. Through the association analysis, we found that nearly 2.6% of human genes were associated with Korean patenting, compared to 20% of human genes in the U.S. patent. The association between the biological functions and the patented sequences indicated that genes whose products act as hormones on defense responses in the extra-cellular environments were the most highly targeted for patenting. The analysis data are available at http://www.patome.net

• Journal of Radiation Industry
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• v.9 no.3
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• pp.161-166
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• 2015

RI-Biomics is the new radiation fusion technology of which, such as the characteristics of radioisotope, is applied to the biomics. In order to sharing and overall analysis of data between the institutions through total management of information in the field of RI-Biomics, RI-Biomics Information portal 'RIBio-Info' was constructed by KARA (Korean Association for Radiation Application) in February 2015. For systematic operation of this 'RIBio-Info' system, it is required to develop system of collection-analysis-application of information. So, in this paper, we summarized development of document forms at each processes of collection-analysis-application of information and systematization of collection methods of information, establishment of characteristically analysis methods of reports such as issue paper, policy report, global market report and watch report. Therefore, these are expected to improving the practical applicability in this field through the vitalization of technology development of users by achieving the circular structure of collectionanalysis-application of information.

• Journal of the Korean Society of Safety
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• v.37 no.6
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• pp.40-49
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• 2022

Simple statistical frequency based analysis, such as Pareto analysis, are widely used in conventional accident analysis. However, due to the dynamic and complex nature of construction works, many factors can simultaneously affect or involve the occurrence of accidents in construction projects. Therefore, the identification of the complex relationship between such factors is important to establish relevant and effective safety management policies and/or programs. In this study, characteristic factors and their relationships' contribution to non-fatal accidents in construction projects are analyzed using the association rule mining (ARM) technique. To this end, a total of 59,202 construction accident data are collected from 2015 to 2019 and the ARM is performed to retrieve specific relationships -named as association rules-among classified factors in the data. Characteristics of the retrieved relationships are analyzed and compared with the results of conventional Pareto analysis. Based on the results, it is found that both fall and trip are notable accident forms having characteristic relations with other factors for non-fatal accidents in construction projects. It is also found that small-scale construction, age of 50s, less than 1 month of working period, and architectural construction are important factors for non-fatal accidents in construction projects.

• Proceedings of the Korea Water Resources Association Conference
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• 2003.05a
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• pp.54-57
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• 2003

• The Journal of the Korean dental association
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• v.39 no.10 s.389
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• pp.849-853
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• 2001

• Proceedings of the Korea Water Resources Association Conference
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• 1996.05a
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• pp.199-204
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• 1996

• Proceedings of the Korea Water Resources Association Conference
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• 1997.05a
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• pp.250-255
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• 1997

• Genomics & Informatics
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• v.10 no.4
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• pp.256-262
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• 2012

Most common complex traits, such as obesity, hypertension, diabetes, and cancers, are known to be associated with multiple genes, environmental factors, and their epistasis. Recently, the development of advanced genotyping technologies has allowed us to perform genome-wide association studies (GWASs). For detecting the effects of multiple genes on complex traits, many approaches have been proposed for GWASs. Multifactor dimensionality reduction (MDR) is one of the powerful and efficient methods for detecting high-order gene-gene ($G{\times}G$) interactions. However, the biological interpretation of $G{\times}G$ interactions identified by MDR analysis is not easy. In order to aid the interpretation of MDR results, we propose a network graph analysis to elucidate the meaning of identified $G{\times}G$ interactions. The proposed network graph analysis consists of three steps. The first step is for performing $G{\times}G$ interaction analysis using MDR analysis. The second step is to draw the network graph using the MDR result. The third step is to provide biological evidence of the identified $G{\times}G$ interaction using external biological databases. The proposed method was applied to Korean Association Resource (KARE) data, containing 8838 individuals with 327,632 single-nucleotide polymorphisms, in order to perform $G{\times}G$ interaction analysis of body mass index (BMI). Our network graph analysis successfully showed that many identified $G{\times}G$ interactions have known biological evidence related to BMI. We expect that our network graph analysis will be helpful to interpret the biological meaning of $G{\times}G$ interactions.

• Asian-Australasian Journal of Animal Sciences
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• v.33 no.9
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• pp.1400-1410
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• 2020

Objective: Genome-wide association study and two meta-analysis based on GWAS performed to explore the genetic mechanism underlying variation in pig number born alive (NBA) and total number born (TNB). Methods: Single trait GWAS and two meta-analysis (single-trait meta analysis and multi-trait meta analysis) were used in our study for NBA and TNB on 3,121 Yorkshires from 4 populations, including three different American Yorkshire populations (n = 2,247) and one British Yorkshire populations (n = 874). Results: The result of single trait GWAS showed that no significant associated single nucleotide polymorphisms (SNPs) were identified. Using single-trait meta analysis and multi-trait meta analysis within populations, 11 significant loci were identified associated with target traits. Spindlin 1, vascular endothelial growth factor A, forkhead box Q1, msh homeobox 1, and LHFPL tetraspan submily member 3 are five functionally plausible candidate genes for NBA and TNB. Compared to the single population GWAS, single-trait Meta analysis can improve the detection power to identify SNPs by integrating information of multiple populations. The multiple-trait analysis reduced the power to detect trait-specific loci but enhanced the power to identify the common loci across traits. Conclusion: In total, our findings identified novel genes to be validated as candidates for NBA and TNB in pigs. Also, it enabled us to enlarge population size by including multiple populations with different genetic backgrounds and increase the power of GWAS by using meta analysis.