• 응용통계연구
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• 제22권3호
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• pp.617-626
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• 2009

최근에 보고되는 일련의 연구는 Affymetrix 마이크로어레이 자료에서 유전자간 상관관계가 강하고 장범위(長範圍)(long-ranged)로 나타나고 있으며, 기존의 "편한" 가정, 즉 유전자간 상관관계가 매우 약하며, 따라서 유전자간 유사 독립성을 가정할 수 있다는 주장이 비현실적이라는 것을 보고하고 있다. Qui 등 (2005b)은 각 유전자의 검정통계량을 병합하여 통계적 추론을 하는 이른바 비모수적 경험적 베이즈 방법을 적용하면 검색된 특이발현 유전자수의 분산이 커진다는 것을 보고하고 있고, 이러한 분산의 불안전성 이유로서 유전자간 강한 상관관계를 지적하고 있다. 또한 Klebanov와 Yakovlev (2007)는 유전자간 상관관계가 통계적 분석을 어렵게 하는 요인이라기 보다는 유용한 정보의 원천이고 적정한 변환을 통하여 근사 독립을 유지할 수 있는 급수를 만들 수 있으며 이 급수를 ${\delta}$-급수라고 불렀다. 본 보고에서는 국내에서 생산된 2조의 cDNA 마이크로어레이 자료에서 유전자간 상관관계가 비교적 강하며, 장범위(長範圍)로 나타나는 것을 확인하며, 유사 독립성을 전제할 수 있는 ${\delta}$-급수가 cDNA 마이크로어레이에서도 발견되는 것을 보고하고자 한다, 동 보고는 추후 cDNA 마이크로어레이 자료의 분석에서도 유전자간 상관관계를 고려하여야 함을 강조하고 있다.

• Genomics & Informatics
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• 제10권2호
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• pp.81-87
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• 2012

Large-scale copy number variants (CNVs) in the human provide the raw material for delineating population differences, as natural selection may have affected at least some of the CNVs thus far discovered. Although the examination of relatively large numbers of specific ethnic groups has recently started in regard to inter-ethnic group differences in CNVs, identifying and understanding particular instances of natural selection have not been performed. The traditional $F_{ST}$ measure, obtained from differences in allele frequencies between populations, has been used to identify CNVs loci subject to geographically varying selection. Here, we review advances and the application of multinomial-Dirichlet likelihood methods of inference for identifying genome regions that have been subject to natural selection with the $F_{ST}$ estimates. The contents of presentation are not new; however, this review clarifies how the application of the methods to CNV data, which remains largely unexplored, is possible. A hierarchical Bayesian method, which is implemented via Markov Chain Monte Carlo, estimates locus-specific $F_{ST}$ and can identify outlying CNVs loci with large values of FST. By applying this Bayesian method to the publicly available CNV data, we identified the CNV loci that show signals of natural selection, which may elucidate the genetic basis of human disease and diversity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권1호
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• pp.240-259
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• 2020

Analyzing network traffic is the basis of dealing with network security issues. Most of the network security systems depend on the feature selection of network traffic data and the detection ability of malicious traffic in network can be improved by the correct method of feature selection. An FAFS method, which is short for Fuzzy Association Feature Selection method, is proposed in this paper for network malicious traffic detection. Association rules, which can reflect the relationship among different characteristic attributes of network traffic data, are mined by association analysis. The membership value of association rules are obtained by the calculation of fuzzy reasoning. The data features with the highest correlation intensity in network data sets are calculated by comparing the membership values in association rules. The dimension of data features are reduced and the detection ability of malicious traffic detection algorithm in network is improved by FAFS method. To verify the effect of malicious traffic feature selection by FAFS method, FAFS method is used to select data features of different dataset in this paper. Then, K-Nearest Neighbor algorithm, C4.5 Decision Tree algorithm and Naïve Bayes algorithm are used to test on the dataset above. Moreover, FAFS method is also compared with classical feature selection methods. The analysis of experimental results show that the precision and recall rate of malicious traffic detection in the network can be significantly improved by FAFS method, which provides a valuable reference for the establishment of network security system.