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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 281 - 290 of 775
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Asymptotic Optimality of Likelihood-Based Cross-Validation
Laan, Mark van der; Dudoit, Sandrine; Keles, Sunduz - In: Statistical Applications in Genetics and Molecular Biology 3 (2009) 1, pp. 4-4
Likelihood-based cross-validation is a statistical tool for selecting a density estimate based on n i.i.d. observations from the true density among a collection of candidate density estimators. General examples are the selection of a model indexing a maximum likelihood estimator, and the...
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Weighted Multiple Hypothesis Testing Procedures
Kang, Guolian; Ye, Keying; Liu, Nianjun; Allison, David; … - In: Statistical Applications in Genetics and Molecular Biology 8 (2009) 1, pp. 23-23
Multiple hypothesis testing is commonly used in genome research such as genome-wide studies and gene expression data analysis (Lin, 2005). The widely used Bonferroni procedure controls the family-wise error rate (FWER) for multiple hypothesis testing, but has limited statistical power as the...
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A Non-Homogeneous Hidden-State Model on First Order Differences for Automatic Detection of Nucleosome Positions
Kuan, Pei Fen; Huebert, Dana; Gasch, Audrey; Keles, Sunduz - In: Statistical Applications in Genetics and Molecular Biology 8 (2009) 1, pp. 29-29
The ability to map individual nucleosomes accurately across genomes enables the study of relationships between dynamic changes in nucleosome positioning/occupancy and gene regulation. However, the highly heterogeneous nature of nucleosome densities across genomes and short linker regions pose...
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Increase of Rejection Rate in Case-Control Studies with the Differential Genotyping Error Rates
Ahn, Kwangmi; Gordon, Derek; Finch, Stephen - In: Statistical Applications in Genetics and Molecular Biology 8 (2009) 1, pp. 25-25
Genotyping error adversely affects the statistical power of case-control association studies and introduces bias in the estimated parameters when the same error mechanism and probabilities apply to both affected and unaffected individuals; that is, when there is non-differential genotype...
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Augmentation Procedures for Control of the Generalized Family-Wise Error Rate and Tail Probabilities for the Proportion of False Positives
Laan, Mark van der; Dudoit, Sandrine; Pollard, Katherine - In: Statistical Applications in Genetics and Molecular Biology 3 (2009) 1, pp. 15-15
This article shows that any single-step or stepwise multiple testing procedure (asymptotically) controlling the family-wise error rate (FWER) can be augmented into procedures that (asymptotically) control tail probabilities for the number of false positives and the proportion of false positives...
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A Comparison of Methods to Control Type I Errors in Microarray Studies
Chen, Jinsong; Laan, Mark van der; Smith, Martyn; … - In: Statistical Applications in Genetics and Molecular Biology 6 (2009) 1, pp. 28-28
Microarray studies often need to simultaneously examine thousands of genes to determine which are differentially expressed. One main challenge in those studies is to find suitable multiple testing procedures that provide accurate control of the error rates of interest and meanwhile are most...
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Supervised Detection of Regulatory Motifs in DNA Sequences
Keles, Sunduz; Laan, Mark van der; Dudoit, Sandrine; … - In: Statistical Applications in Genetics and Molecular Biology 2 (2009) 1, pp. 5-5
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/10005046601
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A Method to Increase the Power of Multiple Testing Procedures Through Sample Splitting
Rubin, Daniel; Dudoit, Sandrine; Laan, Mark van der - In: Statistical Applications in Genetics and Molecular Biology 5 (2009) 1, pp. 19-19
Consider the standard multiple testing problem where many hypotheses are to be tested, each hypothesis is associated with a test statistic, and large test statistics provide evidence against the null hypotheses. One proposal to provide probabilistic control of Type-I errors is the use of...
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Incorporating Duplicate Genotype Data into Linear Trend Tests of Genetic Association: Methods and Cost-Effectiveness
Borchers, Bryce; Brown, Marshall; McLellan, Brian; … - In: Statistical Applications in Genetics and Molecular Biology 8 (2009) 1, pp. 24-24
The genome-wide association (GWA) study is an increasingly popular way to attempt to identify the causal variants in human disease. Duplicate genotyping (or re-genotyping) a portion of the samples in a GWA study is common, though it is typical for these data to be ignored in subsequent tests of...
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Super Learner
Laan, Mark van der; Polley, Eric; Hubbard, Alan - In: Statistical Applications in Genetics and Molecular Biology 6 (2009) 1, pp. 25-25
When trying to learn a model for the prediction of an outcome given a set of covariates, a statistician has many estimation procedures in their toolbox. A few examples of these candidate learners are: least squares, least angle regression, random forests, and spline regression. Previous articles...
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