• Communications for Statistical Applications and Methods
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• v.27 no.5
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• pp.535-546
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• 2020

In genetic association studies, pleiotropy is a phenomenon where a variant or a genetic region affects multiple traits or diseases. There have been many studies identifying cross-phenotype genetic associations. But, most of statistical approaches for detection of pleiotropy are based on individual tests where a single variant association with multiple traits is tested one at a time. These approaches fail to account for relations among correlated variants. Recently, multivariate regularization methods have been proposed to detect pleiotropy in analysis of high-dimensional genomic data. However, they suffer a problem of tuning parameter selection, which often results in either too many false positives or too small true positives. In this article, we applied selection probability to multivariate regularization methods in order to identify pleiotropic variants associated with multiple phenotypes. Selection probability was applied to individual elastic-net, unified elastic-net and multi-response elastic-net regularization methods. In simulation studies, selection performance of three multivariate regularization methods was evaluated when the total number of phenotypes, the number of phenotypes associated with a variant, and correlations among phenotypes are different. We also applied the regularization methods to a wild bean dataset consisting of 169,028 variants and 17 phenotypes.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.3
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• pp.357-367
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• 2020

According to Symantec's 2018 internet security threat report, The number of new mobile malware variants increased by 54 percent in 2017, as compared to 2016. And last year, there were an average of 24,000 malicious mobile applications blocked each day. Existing signature-based technologies of malware detection have limitations. So, malware detection technique through machine learning is being researched to detect malware variant. However, even in the case of applying machine learning, if the proper features of the malware are not properly selected, the machine learning cannot be shown correctly. We are focusing on feature selection method to find the features of malware variant in this research.

• Journal of Microbiology
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• v.37 no.3
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• pp.180-184
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• 1999

hybridoma cells producing IgM anti-pneuococcal 6B polysaccharide antibodies were induced to switch to IgG-producing cells in vitro by treating with acridine orange. Treating 0.5 $\mu\textrm{g}$/ml of acridine orange for 24 hours generated maximal number of variant cells. The maximal isotype switch frequency was 3${\times}$10-5, which is about 30-fold higher than the frequency of spontaneous switching. Resulting IgG-producing variants were enriched by sib selection and ELISA spot assay. Two IgG3-producing variant cells were finally cloned by limiting dilution. The variant cells produced similar amounts of antibodies as their parental cells did. The two switched antibodies had similar reactivity to pneumococcal 6B polysaccharide. When compared to their parental IgM antibodies, the switched IgG3 than that of IgM antibody. The antibodies will be useful as essential tools for comparative study of the role of heavy chain isotypes in protection against Streptococcus pneumoniae.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.18
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• pp.8177-8186
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• 2016

The present study was aimed at determining the effects of alkylating and oxidative stress inducing agents on a newly identified variant of DNA polymerase beta ($pol{\beta}{\Delta}_{208-304}$) specific for ovarian cancer. $Pol{\beta}{\Delta}_{208-304}$ has a deletion of exons 11-13 which lie in the catalytic part of enzyme. We compared the effect of these chemicals on HeLa cells and HeLa cells stably transfected with this variant cloned into in pcDNAI/neo vector by MTT, colony forming and apoptosis assays. $Pol{\beta}{\Delta}_{208-304}$ cells exhibited greater sensitivity to an alkylating agent and less sensitivity towards $H_2O_2$ and UV when compared with HeLa cells alone. It has been shown that cell death in $Pol{\beta}{\Delta}_{208-304}$ transfected HeLa cells is mediated by the caspase 9 cascade. Exon 11 has nucleotidyl selection activity, while exons 12 and 13 have dNTP selection activity. Hence deletion of this part may affect polymerizing activity although single strand binding and double strand binding activity may remain same. The lack of this part may adversely affect catalytic activity of DNA polymerase beta so that the variant may act as a dominant negative mutant. This would represent clinical significance if translated into a clinical setting because resistance to radiation or chemotherapy during the relapse of the disease could be potentially overcome by this approach.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.9-13
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• 2021

Type 2 diabetes mellitus (T2D) is a complex diabetes disease that is caused by high blood sugar, insulin resistance, and a relative lack of insulin. Many studies are trying to predict variant genes that causes this disease by using a sample disease model. In this paper we predict diabetic and normal persons by using fisher score feature selection, chi-2 feature selection and Logistic Regression supervised learning algorithm with best accuracy of 90.23%.

• ETRI Journal
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• v.41 no.5
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• pp.574-584
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• 2019

A new Mirai variant found recently was equipped with a dynamic update ability, which increases the level of difficulty for DDoS mitigation. Continuous development of 5G technology and an increasing number of Internet of Things (IoT) devices connected to the network pose serious threats to cyber security. Therefore, researchers have tried to develop better DDoS mitigation systems. However, the majority of the existing models provide centralized solutions either by deploying the system with additional servers at the host site, on the cloud, or at third party locations, which may cause latency. Since Internet service providers (ISP) are links between the internet and users, deploying the defense system within the ISP domain is the panacea for delivering an efficient solution. To cope with the dynamic nature of the new DDoS attacks, we utilized an unsupervised artificial neural network to develop a hierarchical two-layered self-organizing map equipped with a twofold feature selection for DDoS mitigation within the ISP domain.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.2B
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• pp.130-142
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• 2009

In this paper, we propose a self-adaptation framework that selects a TCP variant adapted to current end-to-end path among available TCP variants. There is no single version of TCP that is suitable to all network environments since the causes for performance degradation are different one another according to characteristics of network environments. Thus, determining that which TCP variants should be selected in order to get best performance is very important. To enable adaptation through such determination, we integrate the existing network estimation schemes and some TCP variants into our framework then make light-weight performance knowledge database for TCP selection. Through implementing and evaluating the proposed framework we show that our solution can help TCP get high and stable performance on the various types of network environments by pure end-to-end.

• Korean Journal of Microbiology
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• v.50 no.1
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• pp.1-7
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• 2014

Site-specific incorporation of unnatural amino acids (SSIUA) into protein can be achieved in vivo by coexpression of an orthogonal pair of suppressor tRNA and engineered aminoacyl-tRNA synthetase (ARS) that specifically ligates an unnatural amino acid to the suppressor tRNA. As a step to develop the SSIUA technique in Escherichia coli, here we established a new 2-step screening system that can be used for selecting an ARS variant(s) that ligates an unnatural amino acid to a suppressor tRNA. A positive selection system consists of chloramphenicol acetyl transferase gene containing an amber mutation at the $27^{th}$ residue, and efficiently concentrated amber suppressible ARS with a maximum enrichment factor of $9.0{\times}10^5$. On the other hand, a negative selection system was constructed by adding multiple amber codons in front of a lethal gene encoding the control of cell death B toxin (ccdB) which acts as an inhibitory protein of bacterial topoisomerase II. Amber suppression of ccdB by an orthogonal pair of Saccharomyces cerevisiae tyrosyl-tRNA synthetase (TyrRS) and an amber suppressor tRNA significantly inhibits bacterial growth. This selection system was also able to efficiently remove amber suppressible ARS which could ligate natural amino acids to the suppressor tRNA. Thus, sequential combination of these two selection systems might be able to function as a powerful tool for selecting an ARS variant that specifically ligates an unnatural amino acid to the suppressor tRNA from an ARS mutant pool.

• ETRI Journal
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• v.33 no.2
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• pp.210-218
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• 2011

Battiti's mutual information feature selector (MIFS) and its variant algorithms are used for many classification applications. Since they ignore feature synergy, MIFS and its variants may cause a big bias when features are combined to cooperate together. Besides, MIFS and its variants estimate feature redundancy regardless of the corresponding classification task. In this paper, we propose an automated greedy feature selection algorithm called conditional mutual information-based feature selection (CMIFS). Based on the link between interaction information and conditional mutual information, CMIFS takes account of both redundancy and synergy interactions of features and identifies discriminative features. In addition, CMIFS combines feature redundancy evaluation with classification tasks. It can decrease the probability of mistaking important features as redundant features in searching process. The experimental results show that CMIFS can achieve higher best-classification-accuracy than MIFS and its variants, with the same or less (nearly 50%) number of features.

• Asian-Australasian Journal of Animal Sciences
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• v.33 no.4
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• pp.539-546
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• 2020

Objective: The Jeju native pig (JNP) found on the Jeju Island of Korea is a unique black pig known for high-quality meat. To investigate the genetic uniqueness of JNP, we analyzed the selection signature of the JNP in comparison to commercial pigs such as Berkshire and Yorkshire pigs. Methods: We surveyed the genetic diversity to identify the genetic stability of the JNP, using the linkage disequilibrium method. A selective sweep of the JNP was performed to identify the selection signatures. To do so, the population differentiation measure, Weir-Cockerham's F_st was utilized. This statistic directly measures the population differentiation at the variant level. Additionally, we investigated the gene ontologies (GOs) and genetic features. Results: Compared to the Berkshire and Yorkshire pigs, the JNP had lower genetic diversity in terms of linkage disequilibrium decays. We summarized the selection signatures of the JNP as GO. In the JNP and Berkshire pigs, the most enriched GO terms were epithelium development and neuron-related. Considering the JNP and Yorkshire pigs, cellular response to oxygen-containing compound and generation of neurons were the most enriched GO. Conclusion: The selection signatures of the JNP were identified through the population differentiation statistic. The genes with possible selection signatures are expected to play a role in JNP's unique pork quality.