Abstract. Our ability to simultaneously measure the expression levels of thousands of genes in biological samples is providing important new opportunities for improving the diagnos...
David M. Reif, Bill C. White, Nancy Olsen, Thomas ...
Background: The incorporation of statistical models that account for experimental variability provides a necessary framework for the interpretation of microarray data. A robust ex...
Kevin A. Greer, Matthew R. McReynolds, Heddwen L. ...
Background: The evaluation of statistical significance has become a critical process in identifying differentially expressed genes in microarray studies. Classical p-value adjustm...
Nitin Jain, HyungJun Cho, Michael O'Connell, Jae K...
: Background Clustering algorithms are widely used in the analysis of microarray data. In clinical studies, they are often applied to find groups of co-regulated genes. Clustering...
Background: Gene expression data frequently contain missing values, however, most downstream analyses for microarray experiments require complete data. In the literature many meth...
Guy N. Brock, John R. Shaffer, Richard E. Blakesle...