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BMC Bioinformatics|April 22, 2021
Gene-set distance analysis (GSDA): a powerful tool for gene-set association analysisXueyuan Cao, Stan PoundsBioinformatics (Oxford, England)|June 17, 2006
Robust estimation of the false discovery rateStan Pounds, Cheng ChengBMC Bioinformatics|June 3, 2014
An R package that automatically collects and archives details for reproducible computingZhifa Liu, Stan PoundsBioinformatics (Oxford, England)|October 6, 2005
Sample size determination for the false discovery rateStan Pounds, Cheng ChengBioinformatics (Oxford, England)|February 28, 2004
Improving false discovery rate estimationStan Pounds, Cheng ChengJournal of Computational Biology : a Journal of Computational Molecular Cell Biology|May 11, 2005
Statistical development and evaluation of microarray gene expression data filtersStan Pounds, Cheng ChengBioinformation|June 29, 2007
False discovery rate paradigms for statistical analyses of microarray gene expression dataCheng Cheng, Stan PoundsInternational Journal of Data Mining and Bioinformatics|April 26, 2011
Integrated analysis of pharmacologic, clinical and SNP microarray data using Projection Onto the Most Interesting Statistical Evidence with Adaptive Permutation TestingStan Pounds, Xueyuan Cao, Cheng Cheng, et al.Bioinformatics (Oxford, England)|July 2, 2003
Estimating the occurrence of false positives and false negatives in microarray studies by approximating and partitioning the empirical distribution of p-valuesStan Pounds, Stephan W MorrisBrain Research|October 24, 2007
Statistical analysis of data from retroviral clonal experiments in the developing retinaStan Pounds, Michael A DyerPageof 19