Sentence examples for multiple testing framework from inspiring English sources

Latent variables have been introduced for various purposes in multiple testing framework.

It would, therefore, be very useful to develop and adopt strategies that allow quantification of the quality of each probeset, to filter out or give little credence to low-quality or unexpressed probesets, and to incorporate these strategies into gene selection within a multiple testing framework.

The second advantage of the Bayesian approach is that it easily allows one to perform sensitivity analysis on model assumptions (see for example Briggs et al., 2006) and, in the multiple testing framework, on prior belief of the null (Westfall et al., 1997).

Controlling the FDR in a multiple hypothesis testing framework is akin to designing a constant false alarm rate (CFAR) detector in spectral target detection applications that keeps the false alarm rate at a desired level irrespective of the background interference and sensor noise statistics[22].

In this study, we focus on the problem of testing for differential expression between groups of arrays, while adjusting for multiple testing in a weighted framework.

For the first part, it relies on a multiple-testing framework to identify with a high sensitivity all the sequences that may have been horizontally transferred between two species at the genome scale.

Then, we developed a multiple-testing framework to evaluate the output of multiple identity-based detections of HTs while controlling for the expected proportion of false positives in the results.

When n is large, the covariance matrix Cov of the LOD scores under the assumption of no QTL can be approximated by (17) where (18) To assess the statistical significance of the EIFs for suspected individuals, we calculate their p-values in the framework of multiple testing problem without identifying the unknown true QTL model.

Yu et al. (2009) provide a hypothesis testing framework for combining multiple SNPs from the same gene or from multiple genes in a pathway-based manner.

We exploring options for in-bot rating of the experience, along with an A/B testing framework for running multiple experiences alongside each other so we can directly measure improvements to the script.

While moving patterns of foraging animals inspire us to define the behavior of a single fuzzer, we are further guided by biology when accumulating multiple fuzzer instances to a parallelized testing framework.