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My bibliography Save this articleUtilizing stability criteria in choosing feature selection methods yields reproducible results in microbiome data
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DOI: 10.1111/biom.13481
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- Wei Lin & Pixu Shi & Rui Feng & Hongzhe Li, 2014. "Variable selection in regression with compositional covariates," Biometrika, Biometrika Trust, vol. 101(4), pages 785-797.
- Jiarui Lu & Pixu Shi & Hongzhe Li, 2019. "Generalized linear models with linear constraints for microbiome compositional data," Biometrics, The International Biometric Society, vol. 75(1), pages 235-244, March.
- Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
- Gregory D. Poore & Evguenia Kopylova & Qiyun Zhu & Carolina Carpenter & Serena Fraraccio & Stephen Wandro & Tomasz Kosciolek & Stefan Janssen & Jessica Metcalf & Se Jin Song & Jad Kanbar & Sandrine Mi, 2020. "RETRACTED ARTICLE: Microbiome analyses of blood and tissues suggest cancer diagnostic approach," Nature, Nature, vol. 579(7800), pages 567-574, March.
- Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
- Monya Baker, 2016. "1,500 scientists lift the lid on reproducibility," Nature, Nature, vol. 533(7604), pages 452-454, May.
- Zachary D Kurtz & Christian L Müller & Emily R Miraldi & Dan R Littman & Martin J Blaser & Richard A Bonneau, 2015. "Sparse and Compositionally Robust Inference of Microbial Ecological Networks," PLOS Computational Biology, Public Library of Science, vol. 11(5), pages 1-25, May.
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