Background: With microarray technology, variability in experimental environments such as RNA sources, microarray production, or the use of different platforms, can cause bias. Suc...
Ki-Yeol Kim, Dong Hyuk Ki, Ha Jin Jeong, Hei-Cheul...
Background: During generation of microarray data, various forms of systematic biases are frequently introduced which limits accuracy and precision of the results. In order to prop...
Background: Microarray measurements are susceptible to a variety of experimental artifacts, some of which give rise to systematic biases that are spatially dependent in a unique w...
High-Seng Chai, Terry M. Therneau, Kent R. Bailey,...
Background: The information from different data sets experimented under different conditions may be inconsistent even though they are performed with the same research objectives. ...
Ki-Yeol Kim, Dong Hyuk Ki, Hei-Cheul Jeung, Hyun C...
Background: To cancel experimental variations, microarray data must be normalized prior to analysis. Where an appropriate model for statistical data distribution is available, a p...