• 한국데이터정보과학회:학술대회논문집
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• 2005.04a
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• pp.164-173
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• 2005

Data mining is the method to find useful information for large amounts of data in database. It is used to find hidden knowledge by massive data, unexpectedly pattern, relation to new rule. The methods of data mining are decision tree, association rules, clustering, neural network and so on. The decision tree approach is most useful in classification problems and to divide the search space into rectangular regions. Decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, fraud detection, data reduction and variable screening, category merging, etc. We analyze waste database united with local information using decision tree techniques for environmental information. We can use these decision tree outputs for environmental preservation and improvement.

• Journal of Genetic Medicine
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• v.20 no.2
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• pp.46-51
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• 2023

Exonic copy number variation (CNV), involving deletions and duplications at the gene's exon level, presents challenges in detection due to their variable impact on gene function. The study delves into the complexities of identifying large CNVs and investigates less familiar but recurrent exonic CNVs, notably enriched in East Asian populations. Examining specific cases like DRC1, STX16, LAMA2, and CFTR highlights the clinical implications and prevalence of exonic CNVs in diverse populations. The review addresses diagnostic challenges, particularly for single exon alterations, advocating for a strategic, multi-method approach. Diagnostic methods, including multiplex ligation-dependent probe amplification, droplet digital PCR, and CNV screening using next-generation sequencing data, are discussed, with whole genome sequencing emerging as a powerful tool. The study underscores the crucial role of ethnic considerations in understanding specific CNV prevalence and ongoing efforts to unravel subtle variations. The ultimate goal is to advance rare disease diagnosis and treatment through ethnically-specific therapeutic interventions.

• FOCUS: LIFE SCIENCE
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• no.1
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• pp.9.1-9.5
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• 2024

Method development for complex low molecular mass (LMM) samples using reversed-phase (RP) separation conditions presents significant challenges due to the presence of many unknown analytes over wide concentration ranges. This guide aims to optimize method parameters-column length (L), temperature (T), flow rate (F), and final mobile phase conditions (Øfinal)-to maximize separation peak capacity. Validated by prior research, this protocol benefits laboratories dealing with metabolomics, natural products, and contaminant screening. This practical guide provides a structured approach to maximizing peak capacity for complex LMM separations. It complements computational optimization strategies and offers a step-by-step method development process. The Snyder-Dolan test is highlighted as essential for determining the need for gradient or isocratic elution and guiding column length decisions. The decision tree framework helps analysts prioritize variable optimization to develop effective separation methods for complex samples.

• Korean Journal of Adult Nursing
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• v.16 no.1
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• pp.72-81
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• 2004

Purpose: The purpose of this study was to examine factors related to different stages of mammography screening based on the transtheoretical model (TTM) and health belief model (HBM). Method: 143 women were recruited from community centers in W city. The mean age was 44.08 (SD=7.78) and 74 (51.7%) had experienced education on preventative behavior related to breast cancer. The Decisional Balance Scale (Pros and Cons of mammography) and Stages of Adoption of Mammography Scale by Rakowski et al. (1992) and the revised Health Belief Model Scale (Perceived Seriousness, Perceived Susceptibility and Health Motivation) by Champion (1993) were used. Result: According to the stage of adoption of mammography, 17.4% of the women were In pre-contemplation, 45.5% in contemplation, 24.5% in action, and 12.6% in maintenance. The mean differences for pros, and the decisional balances between the stages of mammography adoption were significant (F=8.84, p=.000; F=7.20, p=.000). Education related to prevention of breast cancer was the most important variable. Prevention education, history of breast disease and pros of mammography explained the stages of mammography adoption ($R^{2}=26%$). Conclusion: Findings support TTM as a useful tool for improving mammography adherence. Behavioral interventions that target decisional balance and health belief can effectively promote adherence to mammography.

• Journal of The Korean Society of Inherited Metabolic disease
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• v.10 no.1
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• pp.27-30
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• 2010

3-Methylcrotonyl-CoA carboxylase deficiency (3-MCCD) is an autosomal-recessive inborn error of leucine catabolism caused by the deficiency of 3-methylcrotonyl-CoA carboxylase (3-MCC). With the introduction of tandem mass spectrometry in newborn screening, this disorder has been identified with unexpectedly high prevalence. The clinical manifestations of 3-MCCD are highly variable ranging from asymptomatic to severe neurological manifestations. 3-MCC is an heteromeric enzyme consisting of ${\alpha}$ - and ${\beta}$ - subunits, encoded by the MCCC1 and the MCCC2 gene, respectively. In the currentreport, a Korean patient with 3-MCCD is described. She was identified by newborn screening test, and has been asymptomatic with normal development and intelligence up to 3.8 years of age. She carries p.[D280Y]+[D280Y] mutations in the MCCC2 gene.

• The Journal of Information Systems
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• v.18 no.4
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• pp.41-57
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• 2009

Although financial information is a great influence upon determining of the group which use them, detection of management fraud and earning manipulation is a difficult task using normal audit procedures and corporate credit evaluation processes, due to the shortage of knowledge concerning the characteristics of management fraud, and the limitation of time and cost. These limitations suggest the need of systemic process for !he effective risk of earning manipulation for credit evaluators, external auditors, financial analysts, and regulators. Moot researches on management fraud have examined how various characteristics of the company's management features affect the occurrence of corporate fraud. This study examines financial characteristics of companies engaged in fraudulent financial reporting and suggests a model and system for detecting GAAP violations to improve reliability of accounting information and transparency of their management. Since the detection of management fraud has limited proven theory, this study used the detecting method of outlier(upper, and lower bound) financial ratio, as a real-field application. The strength of outlier detecting method is its use of easiness and understandability. In the suggested model, 14 variables of the 7 useful variable categories among the 76 financial ratio variables are examined through the distribution analysis as possible indicators of fraudulent financial statements accounts. The developed model from these variables show a 80.82% of hit ratio for the holdout sample. This model was developed as a financial outlier detecting system for a financial institution. External auditors, financial analysts, regulators, and other users of financial statements might use this model to pre-screen potential earnings manipulators in the credit evaluation system. Especially, this model will be helpful for the loan evaluators of financial institutes to decide more objective and effective credit ratings and to improve the quality of financial statements.

• Journal of Microbiology and Biotechnology
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• v.24 no.3
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• pp.408-420
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• 2014

Yeast surface-displayed antibody libraries provide an efficient and quantitative screening resource for given antigens, but suffer from typically modest library sizes owing to low yeast transformation efficiency. Yeast mating is an attractive method for overcoming the limit of yeast transformation to construct a large, combinatorial antibody library, but the optimal conditions have not been reported. Here, we report a large synthetic human Fab (antigen binding fragment) yeast surface-displayed library generated by stepwise optimization of yeast mating conditions. We first constructed HC (heavy chain) and LC (light chain) libraries, where all of the six CDRs (complementarity-determining regions) of the variable domains were diversified mimicking the human germline antibody repertoires by degenerate codons, onto single frameworks of VH3-23 and $V{\kappa}1$-16 germline sequences, in two haploid cells of opposite mating types. Yeast mating conditions were optimized in the order of cell density, media pH, and cell growth phase, yielding a mating efficiency of ~58% between the two haploid cells carrying HC and LC libraries. We constructed two combinatorial Fab libraries with CDR-H3 of 9 or 11 residues in length with colony diversities of more than $10^9$ by one round of yeast mating between the two haploid HC and LC libraries, with modest diversity sizes of ${\sim}10^7$. The synthetic human Fab yeast-displayed libraries exhibited relative amino acid compositions in each position of the six CDRs that were very similar to those of the designed repertoires, suggesting that they are a promising source for human Fab antibody screening.

• Korean Journal of Veterinary Research
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• v.44 no.4
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• pp.669-676
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• 2004

To evaluate the diagnostic accuracy of two commercial ELISA tests (Allied- and CSL-ELISA) for the diagnosis of Mycobacterium paratuberculosis in cattle, Meta-analysis using English language papers published during 1990-2001 was performed. Diagnostic odds ratios (DOR) were analyzed using regression analysis together with summary receiver operating characteristic (ROC) curves. The difference in diagnostic performance between the two ELISA systems was evaluated by using linear regression. Publication bias was assessed by funnel plot and linear regression. The pooled sensitivity and specificity were 44% (95% CI, 38 to 51) and 98% (95% CI, 96 to 99) for the random-effect model. The DOR between studies was heterogeneous. The area under the fitted ROC curve (AUC) was 0.72 for the unweighted and 0.77 for the weighted model. Maximum joint sensitivity and specificity for the unweighted and weighted model from their summary ROC curve were 70% and 75%, respectively. Based on the fitted model, at a specificity of 95%, sensitivity was estimated to be 52% for the unweighted and 57% for the weighted model. From the final multivariable model study characteristic, the country was the only significant variable with an explained component variance of 13.3%. There were no significant differences in discriminatory power, sensitivity, and specificity between the two ELISA tests. The overall diagnostic accuracy of two commercial ELISA tests was moderate, as judged by the AUC, maximum joint sensitivity and specificity, and estimates from the fitted model and clinical usefulness of the tests for screening program is limited because of low sensitivity and heterogeneous of DOR. It is, therefore, recommended to use ELISA tests as a parallel testing with other diagnostic tests together to increase test sensitivity in the screening program.

• Journal of the Korea Convergence Society
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• v.10 no.3
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• pp.23-30
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• 2019

The purpose of this study is to find out the structure of the substance that affects antidiabetic using the candidate drug information for diabetes treatment. A quantitative structure activity relationship model based on machine learning method was constructed and major molecular descriptors were determined for each experimental data variables from coefficient values using a partial least squares algorithm. The results of the analysis of the molecular access system fingerprint data reflecting the candidate drug structure information were higher than those of the in vitro data analysis in terms of goodness-of-fit, and the major molecular expression factors affecting the antidiabetic effect were also variously derived. If the proposed method is applied to the new drug development environment, it is possible to reduce the cost for conducting candidate screening experiment and to shorten the search time for new drug development.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.5
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• pp.263-270
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• 2021

In this study, a reliability-based design optimization of a 130-m class fixed-type offshore platform, to be installed in the North Sea, was carried out, while considering environmental, material, and manufacturing uncertainties to enhance its structural safety and economic aspects. For the reliability analysis, and reliability-based design optimization of the structural integrity, unity check values (defined as the ratio between working and allowable stress, for axial, bending, and shear stresses), of the members of the offshore platform were considered as constraints. Weight of the supporting jacket structure was minimized to reduce the manufacturing cost of the offshore platform. Statistical characteristics of uncertainties were defined based on observed and measured data references. Reliability analysis and reliability-based design optimization of a jacket-type offshore structure were computationally burdensome due to the large number of members; therefore, we suggested a method for variable screening, based on the importance of their output responses, to reduce the dimension of the problem. Furthermore, a deterministic design optimization was carried out prior to the reliability-based design optimization, to improve overall computational efficiency. Finally, the optimal design obtained was compared with the conventional rule-based offshore platform design in terms of safety and cost.