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  • Search: isPartOf:"Statistical Applications in Genetics and Molecular Biology"
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multiple testing 28 gene expression 21 microarray 15 microarrays 14 bootstrap 11 false discovery rate 11 classification 10 null distribution 10 variable selection 10 cross-validation 9 Type I error rate 8 asymptotic control 8 model selection 8 prediction 7 Adjusted p-value 6 empirical Bayes 6 Microarrays 5 censoring 5 consistency 5 differential expression 5 machine learning 5 maximum likelihood 5 meta-analysis 5 multiple comparisons 5 normalization 5 EM algorithm 4 FDR 4 Markov chain Monte Carlo 4 SNP 4 augmentation 4 case-control 4 clustering 4 cut-off 4 family-wise error rate 4 generalized family-wise error rate 4 genetics 4 hidden Markov model 4 loss-based estimation 4 mass spectrometry 4 microarray analysis 4
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Undetermined 775
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Article 775
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Laan, Mark van der 35 van der Laan Mark J. 19 Dudoit, Sandrine 12 Hubbard, Alan 10 Pollard, Katherine 9 Rongling, Wu 9 Sinisi, Sandra 8 Bickel David R. 7 Keles, Sunduz 7 R, Segal Mark 7 Sandrine, Dudoit 7 Birkner, Merrill 6 Derek, Gordon 6 Dirk, Husmeier 6 J, Finch Stephen 6 Joseph, Beyene 6 Segal, Mark 6 Hubbard Alan E. 5 Sunduz, Keles 5 Thomas, Lengauer 5 Tomasz, Burzykowski 5 Ziv, Shkedy 5 Beyene, Joseph 4 Bickel, David 4 Boulesteix, Anne-Laure 4 Brad, McNeney 4 Burzykowski, Tomasz 4 Chad, Haynes 4 Dan, Lin 4 Eisen, Michael 4 Hongyu, Zhao 4 Jinko, Graham 4 Paul, Joyce 4 Pollard Katherine S. 4 Polley, Eric 4 Shkedy, Ziv 4 Smith, Martyn 4 Sylvia, Richardson 4 Tibshirani Robert J. 4 Wu, Rongling 4
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Statistical Applications in Genetics and Molecular Biology 775
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RePEc 775
Showing 771 - 775 of 775
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Sampling Correction in Pedigree Analysis
Emil, Ginsburg; Ida, Malkin; C, Elston Robert - In: Statistical Applications in Genetics and Molecular Biology 2 (2003) 1, pp. 1-24
Usually, a pedigree is sampled and included in the sample that is analyzed after following a predefined non-random sampling design comprising several specific procedures. To obtain a pedigree analysis result free from the bias caused by the sampling procedures, a correction is applied to the...
Persistent link: https://www.econbiz.de/10008460235
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Parameter estimation for the calibration and variance stabilization of microarray data
Wolfgang, Huber; Anja, von Heydebreck; Holger, Sueltmann; … - In: Statistical Applications in Genetics and Molecular Biology 2 (2003) 1, pp. 1-24
We derive and validate an estimator for the parameters of a transformation for the joint calibration (normalization) and variance stabilization of microarray intensity data. With this, the variances of the transformed intensities become approximately independent of their expected values. The...
Persistent link: https://www.econbiz.de/10008460285
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Supervised Detection of Regulatory Motifs in DNA Sequences
Sunduz, Keles; van der Laan Mark J.; Sandrine, Dudoit; … - In: Statistical Applications in Genetics and Molecular Biology 2 (2003) 1, pp. 1-40
Identification of transcription factor binding sites (regulatory motifs) is a major interest in contemporary biology. We propose a new likelihood based method, COMODE, for identifying structural motifs in DNA sequences. Commonly used methods (e.g. MEME, Gibbs motif sampler) model binding sites...
Persistent link: https://www.econbiz.de/10008460306
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An additive genetic gamma frailty model for two-locus linkage analysis using sibship age of onset data
Xiaoyun, Zhong; Hongzhe, Li - In: Statistical Applications in Genetics and Molecular Biology 1 (2002) 1, pp. 1-26
Complex human diseases are often due to multiple disease genes with variable age of onset, as well as both genetic and environmental risk factors. In order to simultaneously consider two possible disease loci and to incorporate age of onset and covariate information into genetic linkage...
Persistent link: https://www.econbiz.de/10008460233
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Pre-validation and inference in microarrays
Tibshirani Robert J.; Brad, Efron - In: Statistical Applications in Genetics and Molecular Biology 1 (2002) 1, pp. 1-20
In microarray studies, an important problem is to compare a predictor of disease outcome derived from gene expression levels to standard clinical predictors. Comparing them on the same dataset that was used to derive the microarray predictor can lead to results strongly biased in favor of the...
Persistent link: https://www.econbiz.de/10008460331
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