Gilbert, Houston N. Working Paper Advanced Search. Privacy Copyright. Skip to main content. Collection of Biostatistics Research Archive. Turner, S. R package version 0. Dowle, M.
- Chemokines, Part A.
- Bioconductor - multtest.
- Multiple Testing Procedures with Applications to Genomics : Sandrine Dudoit : ?
- Multiple Inference?
- Recognition and Power: Axel Honneth and the Tradition of Critical Social Theory?
- Possibility, Necessity, and Existence: Abbagnano and His Predecessors?
Warnes, G. R package version 3. Clayton, D.
Resampling-based multiple hypothesis testing
Mittag, F. Use of support vector machines for disease risk prediction in genome-wide association studies: Concerns and opportunities. Davies, R. Evans, D. Harnessing the information contained within genome-wide association studies to improve individual prediction of complex disease risk. Ioannidis, J. Kooperberg, C. Risk prediction using genome-wide association studies. Quevedo, J. Wei, Z. PLoS Genet. The genetic interpretation of area under the ROC curve in genomic profiling.
Austin, E. Penalized regression and risk prediction in genome-wide association studies. Data Min. Okser, S. Regularized machine learning in the genetic prediction of complex traits.
Genetics and Genomics
Wu, Q. Schwarz, D. On safari to Random Jungle: a fast implementation of Random Forests for high-dimensional data. Bioinformatics 26 , — Rakitsch, B. A Lasso multi-marker mixed model for association mapping with population structure correction. Bioinformatics 29 , — Shi, G.
Mining gold dust under the genome wide significance level: a two-stage approach to analysis of GWAS. Stability selection. B Statistical Methodol. Pahikkala, T. Wrapper-based selection of genetic features in genome-wide association studies through fast matrix operations. Algorithms Mol. He, Q. A variable selection method for genome-wide association studies. Bioinformatics 27 , 1—8 Zhou, H. Association screening of common and rare genetic variants by penalized regression. Minnier, J.
Risk classification with an adaptive naive Bayes Kernel machine model.
Nguyen, T. Genome-wide association data classification and SNPs selection using two-stage quality-based Random Forests. BMC Genomics 16 , S5 Tsai, M. Variable selection in Bayesian generalized linear-mixed models: An illustration using candidate gene case-control association studies. Biometrical Journal 57 , — Manor, O. Predicting disease risk using bootstrap ranking and classification algorithms. PLoS Comput. Hoffman, G. Fisher, C. Bayesian feature selection for high-dimensional linear regression via the Ising approximation with applications to genomics. Bioinformatics 11 , — Breiman, L.
Random forests. Machine learning 45 , 5—32 Zou, H. Regularization and variable selection via the elastic net. Fan, J. Sure independence screening for ultrahigh dimensional feature space. Li, J. A fast algorithm for detecting gene—gene interactions in genome-wide association studies.
The annals of applied statistics 8 , FaST linear mixed models for genome-wide association studies. Methods 8 , — Mimno, D. Posterior predictive checks to quantify lack-of-fit in admixture models of latent population structure. Loh, P. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Song, M. Testing for genetic associations in arbitrarily structured populations. Zhou, X. Efficient multivariate linear mixed model algorithms for genome-wide association studies. Methods 11 , — Kang, H. Efficient control of population structure in model organism association mapping.
Genetics , — Download references. This paper is part of a larger project on the genetics of social and economic behavior. The idea for this paper arose in the workshop that regularly takes place in the context of this project at the University of Zurich, and which is based on the collaboration of teams at universities in Berlin, Barcelona, Mainz, and Zurich. This work is licensed under a Creative Commons Attribution 4.
Current Genetic Medicine Reports Journal of Thoracic Oncology Frontiers in Genetics Frontiers in Oncology BMC Systems Biology By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate. Article metrics. Advanced search. Skip to main content.
Subjects Computational science Genome-wide association studies Statistical methods. Abstract The standard approach to the analysis of genome-wide association studies GWAS is based on testing each position in the genome individually for statistical significance of its association with the phenotype under investigation. The core idea is a two-step algorithm consisting of a machine learning and SNP selection step that drastically reduces the number of candidate SNPs by selecting only a small subset of the most predictive SNPs; and a statistical testing step where only the SNPs selected in step 1 are tested for association.
Full size image. Machine learning and SNP-selection step colored in red. Statistical testing step colored in blue. Problem Setting and Methodology In this section, we formally describe the statistical problem under investigation and propose a novel methodology for tackling it — based on a combination of machine learning and statistical testing techniques. Problem Setting and Notation Let n denote the number of subjects in the study and d the number of SNPs under investigation.
Table 1: Tabular representation of single SNP data. Full size table. Results Validation Validation using simulated phenotypes To assess the performance of the proposed COMBI method in comparison to other methods in a controlled environment, we conducted a number of simulation experiments with semi-real data. Figure 2: Illustration of validation methodology. Figure 3: Genome-wide scan for seven diseases. Discussion Several related machine learning methods have been successfully used in the context of statistical genomics.
These approaches can be classified into two groups: Methods that construct a model from genetic data in order to carry out accurate predictions on a phenotype 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , Additional Information How to cite this article : Mieth, B.preprod-rep.brightnetwork.co.uk/ducir-como-conocer-gente.php
Multiple Testing Procedures with Applications to Genomics | Sandrine Dudoit | Springer
References 1. PubMed Article Google Scholar 8. PubMed Article Google Scholar PubMed Google Scholar Article Google Scholar Google Scholar Acknowledgements This paper is part of a larger project on the genetics of social and economic behavior. Supplementary information PDF files 1. Supplementary Information. Ebooks cut down on the employ of paper, as advocated by environmental enthusiasts. Right now there are no fixed timings for study. There will be no question of waiting-time for new editions. Right now there is no transportation to be able to the eBook shop. The particular books in an eBook store can be downloaded instantly, sometimes for free, at times for any fee.
Not merely that, the online edition of books are typically less expensive, because publication houses save on their print plus paper machinery, the benefits of which are given to to customers.