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What is False Discovery Rate (FDR)

Encyclopedia of Artificial Intelligence
FDR controls the expected proportion of false positives instead of controlling the chance of any false positives. A FDR threshold is determined from the observed p-value distribution from multiple single hypothesis tests.
Published in Chapter:
CNS Tumor Prediction Using Gene Expression Data Part II
Atiq Islam (University of Memphis, USA), Khan M. Iftekharuddin (University of Memphis, USA), E. Olusegun George (University of Memphis, USA), and David J. Russomanno (University of Memphis, USA)
Copyright: © 2009 |Pages: 6
DOI: 10.4018/978-1-59904-849-9.ch048
Abstract
In this chapter, we propose a novel algorithm for characterizing a variety of CNS tumors. The proposed algorithm is illustrated with an analysis of an Affymetrix gene expression data from CNS tumor samples (Pomeroy et al., 2002). As discussed in the previous chapter entitled: CNS Tumor Prediction Using Gene Expression Data Part I, we used an ANOVA model to normalize the microarray gene expression measurements. In this chapter, we introduce a systemic way of building tumor prototypes to facilitate automatic prediction of CNS tumors.
Full Text Chapter Download: US $37.50 Add to Cart
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CNS Tumor Prediction Using Gene Expression Data Part I
Controls the expected proportion of false positives instead of controlling the chance of any false positives. An FDR threshold is determined from the observed p-value distribution from multiple single hypothesis tests.
Full Text Chapter Download: US $37.50 Add to Cart
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