• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.235-235
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• 2022

Genome selection is a promising tool for plant and animal breeding, which uses genome-wide molecular marker data to capture large and small effect quantitative trait loci and predict the genetic value of selection candidates. Genomic selection has been shown previously to have higher prediction accuracies than conventional marker-assisted selection (MAS) for quantitative traits. In this study, the prediction accuracy of 10 agricultural traits in the wheat core group with 567 points was compared. We used a cross-validation approach to train and validate prediction accuracy to evaluate the effects of training population size and training model.As for the prediction accuracy according to the model, the prediction accuracy of 0.4 or more was evaluated except for the SVN model among the 6 models (GBLUP, LASSO, BayseA, RKHS, SVN, RF) used in most all traits. For traits such as days to heading and days to maturity, the prediction accuracy was very high, over 0.8. As for the prediction accuracy according to the training group, the prediction accuracy increased as the number of training groups increased in all traits. It was confirmed that the prediction accuracy was different in the training population according to the genetic composition regardless of the number. All training models were verified through 5-fold cross-validation. To verify the prediction ability of the training population of the wheat core collection, we compared the actual phenotype and genomic estimated breeding value using 35 breeding population. In fact, out of 10 individuals with the fastest days to heading, 5 individuals were selected through genomic selection, and 6 individuals were selected through genomic selection out of the 10 individuals with the slowest days to heading. Therefore, we confirmed the possibility of selecting individuals according to traits with only the genotype for a shorter period of time through genomic selection.

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.1
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• pp.19-25
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• 2018

Objective: In practical breeding, selection is often performed by ignoring the accuracy of evaluations and applying economic weights directly to the selection index coefficients of genetically standardized traits. The denominator of the standardized component trait of estimated genetic evaluations in practical selection varies with its reliability. Whereas theoretical methods for calculating the selection index coefficients of genetically standardized traits account for this variation, practical selection ignores reliability and assumes that it is equal to unity for each trait. The purpose of this study was to clarify the effects of ignoring the accuracy of the standardized component trait in selection criteria on selection responses and economic weights in retrospect. Methods: Theoretical methods were presented accounting for reliability of estimated genetic evaluations for the selection index composed of genetically standardized traits. Results: Selection responses and economic weights in retrospect resulting from practical selection were greater than those resulting from theoretical selection accounting for reliability when the accuracy of the estimated breeding value (EBV) or genomically enhanced breeding value (GEBV) was lower than those of the other traits in the index, but the opposite occurred when the accuracy of the EBV or GEBV was greater than those of the other traits. This trend was more conspicuous for traits with low economic weights than for those with high weights. Conclusion: Failure of the practical index to account for reliability yielded economic weights in retrospect that differed from those obtained with the theoretical index. Our results indicated that practical indices that ignore reliability delay genetic improvement. Therefore, selection practices need to account for reliability, especially when the reliabilities of the traits included in the index vary widely.

• Asian-Australasian Journal of Animal Sciences
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• v.33 no.12
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• pp.1912-1921
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• 2020

Objective: This study assessed genomic prediction accuracies based on different selection methods, evaluation procedures, training population (TP) sizes, heritability (h²) levels, marker densities and pedigree error (PE) rates in a simulated Korean beef cattle population. Methods: A simulation was performed using two different selection methods, phenotypic and estimated breeding value (EBV), with an h² of 0.1, 0.3, or 0.5 and marker densities of 10, 50, or 777K. A total of 275 males and 2,475 females were randomly selected from the last generation to simulate ten recent generations. The simulation of the PE dataset was modified using only the EBV method of selection with a marker density of 50K and a heritability of 0.3. The proportions of errors substituted were 10%, 20%, 30%, and 40%, respectively. Genetic evaluations were performed using genomic best linear unbiased prediction (GBLUP) and single-step GBLUP (ssGBLUP) with different weighted values. The accuracies of the predictions were determined. Results: Compared with phenotypic selection, the results revealed that the prediction accuracies obtained using GBLUP and ssGBLUP increased across heritability levels and TP sizes during EBV selection. However, an increase in the marker density did not yield higher accuracy in either method except when the h² was 0.3 under the EBV selection method. Based on EBV selection with a heritability of 0.1 and a marker density of 10K, GBLUP and ssGBLUP_0.95 prediction accuracy was higher than that obtained by phenotypic selection. The prediction accuracies from ssGBLUP_0.95 outperformed those from the GBLUP method across all scenarios. When errors were introduced into the pedigree dataset, the prediction accuracies were only minimally influenced across all scenarios. Conclusion: Our study suggests that the use of ssGBLUP_0.95, EBV selection, and low marker density could help improve genetic gains in beef cattle.

• Journal of information and communication convergence engineering
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• v.21 no.2
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• pp.130-138
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• 2023

The selection of the correct examination variables for diagnosing heart disease provides many benefits, including faster diagnosis and lower cost of examination. The selection of inspection variables can be performed by referring to the data of previous examination results so that future investigations can be carried out by referring to these selected variables. This paper proposes a model for selecting examination variables using an Artificial Bee Swarm Optimization method by considering the variables of accuracy and cost of inspection. The proposed feature selection model was evaluated using the performance parameters of accuracy, area under curve (AUC), number of variables, and inspection cost. The test results show that the proposed model can produce 24 examination variables and provide 95.16% accuracy and 97.61% AUC. These results indicate a significant decrease in the number of inspection variables and inspection costs while maintaining performance in the excellent category.

• Journal of Biomedical Engineering Research
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• v.42 no.6
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• pp.268-276
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• 2021

Functional near-infrared spectroscopy-based brain-computer interface (fNIRS-based BCI) has been receiving much attention. However, we are practically constrained to obtain a lot of fNIRS data by inherent hemodynamic delay. For this reason, when employing machine learning techniques, a problem due to the high-dimensional feature vector may be encountered, such as deteriorated classification accuracy. In this study, we employ an elastic net-based feature selection which is one of the embedded methods and demonstrate the utility of which by analyzing the results. Using the fNIRS dataset obtained from 18 participants for classifying brain activation induced by mental arithmetic and idle state, we calculated classification accuracies after performing feature selection while changing the parameter α (weight of lasso vs. ridge regularization). Grand averages of classification accuracy are 80.0 ± 9.4%, 79.3 ± 9.6%, 79.0 ± 9.2%, 79.7 ± 10.1%, 77.6 ± 10.3%, 79.2 ± 8.9%, and 80.0 ± 7.8% for the various values of α = 0.001, 0.005, 0.01, 0.05, 0.1, 0.2, and 0.5, respectively, and are not statistically different from the grand average of classification accuracy estimated with all features (80.1 ± 9.5%). As a result, no difference in classification accuracy is revealed for all considered parameter α values. Especially for α = 0.5, we are able to achieve the statistically same level of classification accuracy with even 16.4% features of the total features. Since elastic net-based feature selection can be easily applied to other cases without complicated initialization and parameter fine-tuning, we can be looking forward to seeing that the elastic-based feature selection can be actively applied to fNIRS data.

• Journal of Embryo Transfer
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• v.2 no.1
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• pp.22-26
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• 1987

When the dairy cattle are genetically improved by embryo transfer, generation intervals can be reduced since sires are selected by their full-sister's records rather than by their daughter's records and selection intensity increases because only donor cows and sires for them are selected. In addition by embryo transfer many number of full-sisters and full-sisters are produced at the same time, resulting in the increase in the accuracy of selection.

• Asian-Australasian Journal of Animal Sciences
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• v.30 no.2
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• pp.149-153
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• 2017

Objective: This study was to determine the relationship between estimated breeding value and phenotype information after farrowing when juvenile selection was made in candidate pigs without phenotype information. Methods: After collecting phenotypic and genomic information for the total number of piglets born by Landrace pigs, selection accuracy between genomic breeding value estimates using genomic information and breeding value estimates of best linear unbiased prediction (BLUP) using conventional pedigree information were compared. Results: Genetic standard deviation (${\sigma}_a$) for the total number of piglets born was 0.91. Since the total number of piglets born for candidate pigs was unknown, the accuracy of the breeding value estimated from pedigree information was 0.080. When genomic information was used, the accuracy of the breeding value was 0.216. Assuming that the replacement rate of sows per year is 100% and generation interval is 1 year, genetic gain per year is 0.346 head when genomic information is used. It is 0.128 when BLUP is used. Conclusion: Genetic gain estimated from single step best linear unbiased prediction (ssBLUP) method is by 2.7 times higher than that the one estimated from BLUP method, i.e., 270% more improvement in efficiency.

• Journal of Information Processing Systems
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• v.19 no.5
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• pp.652-662
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• 2023

Identifying highly discriminating genes is a critical step in tumor recognition tasks based on microarray gene expression profile data and machine learning. Gene selection based on tree models has been the subject of several studies. However, these methods are based on a single-tree model, often not robust to ultra-highdimensional microarray datasets, resulting in the loss of useful information and unsatisfactory classification accuracy. Motivated by the limitations of single-tree-based gene selection, in this study, ensemble gene selection methods based on multiple-tree models were studied to improve the classification performance of tumor identification. Specifically, we selected the three most representative tree models: ID3, random forest, and gradient boosting decision tree. Each tree model selects top-n genes from the microarray dataset based on its intrinsic mechanism. Subsequently, three ensemble gene selection methods were investigated, namely multipletree model intersection, multiple-tree module union, and multiple-tree module cross-union, were investigated. Experimental results on five benchmark public microarray gene expression datasets proved that the multiple tree module union is significantly superior to gene selection based on a single tree model and other competitive gene selection methods in classification accuracy.

• Progress in Superconductivity and Cryogenics
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• v.23 no.3
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• pp.26-31
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• 2021

Advanced classification of Cs contaminated soil by using a magnetic force-assisted selection pipe was investigated. A selection pipe is a device that sort particles depending on their particle size, based on the relationship between buoyancy, drag, and gravity force acting on the particles. Radioactive cesium is concentrated in small-particle size soil components with a large specific surface area. Hence, the volume of the Cs contaminated soil can be reduced by recycling the large-particle size soil components with low radioactive concentration. One of the problems of the selection pipe was that the radioactive concentration of the stayed soil in the selection pipe exceeds 8000 Bq/kg, which is the standard value of recycling of Cs contaminated soil, due to low classification accuracy. In this study, magnetic fields were applied to the lab-scale selection pipe from upper side to improve the classification accuracy and to reduce the radioactive concentration of the stayed soil.

• Journal of Information Technology Applications and Management
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• v.15 no.1
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• pp.271-282
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• 2008

Database administrators are demanded to acquire much knowledges and take great efforts for keeping consistent performance in system. Various principles, methods, and tools have been proposed in many studies and commercial products in order to alleviate such burdens on database administrators, and it has resulted to the automation of DBMS which reduces the intervention of database administrator. This paper suggests a resource selection method that estimates the status of the database system based on the workload characteristics and that recommends tuneable resources. Our method tries to simplify selection information on DBMS status using data-mining techniques, enhance the accuracy of the selection model, and recommend tuneable resource. For evaluating the performance of our method, instances are collected in TPC-C and TPC-W workloads, and accuracy are calculated using 10 cross validation method, comparisons are made between our scheme and the method which uses only the classification procedure without any simplification of informations. It is shown that our method has over 90% accuracy and can perform tuneable resource selection.