Statistical Applications in Genetics and Molecular Biology
Editor-in-Chief: Stumpf, Michael P.H.
IMPACT FACTOR increased in 2014: 1.127
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Rank 47 out of 122 in category Statistics & Probability in the 2014 Thomson Reuters Journal Citation Report/Science Edition
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Impact per Publication (IPP) 2014: 0.926
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Most Downloaded Articles
- A General Framework for Weighted Gene Co-Expression Network Analysis by Zhang, Bin and Horvath, Steve
- Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments by Smyth, Gordon K
- Sensitivity to prior specification in Bayesian genome-based prediction models by Lehermeier, Christina/ Wimmer, Valentin/ Albrecht, Theresa/ Auinger, Hans-Jürgen/ Gianola, Daniel/ Schmid, Volker J. and Schön, Chris-Carolin
- A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics by Schäfer, Juliane and Strimmer, Korbinian
- Detecting Differential Expression in RNA-sequence Data Using Quasi-likelihood with Shrunken Dispersion Estimates by Lund, Steven P./ Nettleton, Dan/ McCarthy, Davis J. and Smyth, Gordon K.
Pre-validation and inference in microarrays
Citation Information: Statistical Applications in Genetics and Molecular Biology. Volume 1, Issue 1, ISSN (Online) 1544-6115, DOI: 10.2202/1544-6115.1000, August 2002
- Published Online:
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 microarray predictor. We propose a new technique called ``pre-validation'' for making a fairer comparison between the two sets of predictors. We study the method analytically and explore its application in a recent study on breast cancer.
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