Sentence examples for multiple testing datasets from inspiring English sources

Unlike other algorithms, we observed that the classification performance of our IPRE algorithm on multiple testing datasets was consistently good, suggesting that the IPRE algorithm was independent of any testing dataset when it achieved high classification performance.

All the results were obtained over an average of ten trials, and we have verified that the exact same targets were predicted in both CUDA- miRanda and the original miRanda implementations through multiple testing datasets.

Experimental results show that our deep network is able to extract useful features for the illumination estimation and our method outperforms all previous color constancy methods on multiple test datasets.

This result was confirmed using a jackknife method, where multiple test datasets were generated by leave-one-out from the Stat3 dataset.

In addition, we have verified that the exact same targets were predicted in both CUDA-miRanda and the original miRanda implementations through multiple test datasets.

In order to use survival analysis to validate the public signatures across multiple test datasets, we collected 31 breast cancer datasets containing both clinical survival data and gene-expression data.

Because it is unclear how to properly correct for multiple testing in these datasets, we opted to require replication in a second cohort.

However, in addition to the disadvantages discussed previously (curse of dimensionality, assumption violations, computational and multiple testing burdens with large datasets) that make exhaustive regression-based approaches impractical, it is also difficult to incorporate a priori information into a parametric regression analysis as it has been done here.

The most important drawbacks of such subgroup analyses are related to sample size (each subgroup being smaller than the whole dataset) and multiple testing issues (if several subgroups are investigated in turn).

None were significant after correction for multiple testing (P <0.00042 (correction factor of 120; 10 datasets × 12 SNPs)).

We found no significant associations with individual SNPs within VDR or TGFBR1 before or after correction for multiple tests, although our dataset did not include the VDR FokI polymorphism [ 8] or TGFBR1*6A variant [ 21], or at the gene level.