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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국자료분석학회 Journal of The Korean Data Analysis Society Journal of The Korean Data Analysis Society 제20권 제6호
- 발행연도
- 2018.1
- 수록면
- 2,711 - 2,719 (9page)
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초록· 키워드
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When an observable is described by a single value, the statistic significance may be estimated by construction of null distribution using permutation and counting the portion of it that exceeds the observed value by chance. Genome-wide association study usually focuses on the association measure between a single or interacting genotypes with a single phenotype. However investigation of common genotypes associated simultaneously on multiple phenotypes may involve the observables that should be described with multiple numbers. Statistical significance for such an observable would involve null distribution in multiple dimensions. In this study, extension of the p-value estimation process using null distribution in one dimension has been sought that may be applicable to two dimensional case. Comparison of the position of points within the set of points they form has been proposed to use a positioning parameter inspired by the extension of the Kolmogorov- Smirnov statistic to two dimensions.
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등록된 정보가 없습니다.
참고문헌 (24)
참고문헌 신청
Beirlant, J. / 1997 / Nonparametric entropy estimation: An overview / International Journal of Mathematical and Statistical Sciences 6:17~39
Brunel, H. / 2010 / MISS: a non-linear methodology based on mutual information for genetic association studies in both population and sib-pairs analysis / Bioinformatics 26(15):1811~1818
Bush, W. S. / 2012 / Chapter 11: Genome-wide association studies / PLoS Computational Biology 8(12):e1002822
Cho, Y. S. / 2009 / A large-scale genome-wide association study of Asian populations uncovers genetic factors influencing eight quantitative trait / Nature Genetics 41(5):527~534
Denny, J. C. / 2016 / Phenome-wide association studies as a tool to advance precision medicine / Annual Review of Genomics and Human Genetics 17:353~373
Brunel, H. / 2010 / MISS: a non-linear methodology based on mutual information for genetic association studies in both population and sib-pairs analysis / Bioinformatics 26(15):1811~1818
Bush, W. S. / 2012 / Chapter 11: Genome-wide association studies / PLoS Computational Biology 8(12):e1002822
Cho, Y. S. / 2009 / A large-scale genome-wide association study of Asian populations uncovers genetic factors influencing eight quantitative trait / Nature Genetics 41(5):527~534
Denny, J. C. / 2016 / Phenome-wide association studies as a tool to advance precision medicine / Annual Review of Genomics and Human Genetics 17:353~373
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