Comparing the performance of linear and nonlinear principal components in the context of high-dimensional genomic data integration
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DOI: 10.1515/sagmb-2016-0066
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- Xiaobo Guo & Ye Zhang & Wenhao Hu & Haizhu Tan & Xueqin Wang, 2014. "Inferring Nonlinear Gene Regulatory Networks from Gene Expression Data Based on Distance Correlation," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-7, February.
- Jessica Minnier & Ming Yuan & Jun S. Liu & Tianxi Cai, 2015. "Risk Classification With an Adaptive Naive Bayes Kernel Machine Model," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 393-404, March.
- Diana Chang & Alon Keinan, 2014. "Principal Component Analysis Characterizes Shared Pathogenetics from Genome-Wide Association Studies," PLOS Computational Biology, Public Library of Science, vol. 10(9), pages 1-14, September.
- Aguilera, Ana M. & Escabias, Manuel & Valderrama, Mariano J., 2006. "Using principal components for estimating logistic regression with high-dimensional multicollinear data," Computational Statistics & Data Analysis, Elsevier, vol. 50(8), pages 1905-1924, April.
- Karatzoglou, Alexandros & Smola, Alexandros & Hornik, Kurt & Zeileis, Achim, 2004. "kernlab - An S4 Package for Kernel Methods in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 11(i09).
- W. Gibson, 1959. "Three multivariate models: Factor analysis, latent structure analysis, and latent profile analysis," Psychometrika, Springer;The Psychometric Society, vol. 24(3), pages 229-252, September.
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Keywords
AUC; Copula; Gamma distribution; Kernel PCA; principal component;All these keywords.
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