It appears you don't have support to open PDFs in this web browser. To view this file, Open with your PDF reader
Abstract
There is much interest in using genome-wide expression time series to identify circadian genes. Several methods have been developed to test for rhythmicity in sparsely sampled time series typical of such measurements. Because these methods are statistical in nature, they rely on estimating the probabilities that patterns arise by chance (i.e., p-values). Here we show that leading methods implicitly make inappropriate assumptions of independence when estimating p-values. We show how to correct for the dependence to obtain accurate estimates for statistical significance during rhythm detection.
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer