Finding quantitative trait loci genes with collaborative targeted maximum likelihood learning
Quantitative trait loci mapping is focused on identifying the positions and effect of genes underlying an observed trait. We present a collaborative targeted maximum likelihood estimator in a semi-parametric model using a newly proposed 2-part super learning algorithm to find quantitative trait loci genes in listeria data. Results are compared to the parametric composite interval mapping approach.
Volume (Year): 81 (2011)
Issue (Month): 7 (July)
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- van der Laan Mark J. & Rubin Daniel, 2006. "Targeted Maximum Likelihood Learning," The International Journal of Biostatistics, De Gruyter, vol. 2(1), pages 1-40, December.
- van der Laan Mark J. & Polley Eric C & Hubbard Alan E., 2007. "Super Learner," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 6(1), pages 1-23, September.
- Sinisi Sandra E & van der Laan Mark J., 2004. "Deletion/Substitution/Addition Algorithm in Learning with Applications in Genomics," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-40, August.
- Jin, Chunfang & Fine, Jason P. & Yandell, Brian S., 2007. "A Unified Semiparametric Framework for Quantitative Trait Loci Analyses, With Application to Spike Phenotypes," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 56-67, March.
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