Sentence examples for methods we test from inspiring English sources

In addition, the simulation time of CE/SE is about 10percentt lower than the other second and third order methods we test.

There is no clear winner among the methods we test in terms of both accuracy and reproducibility, and our results highlight tradeoffs between accuracy and reproducibility, which are bias variance tradeoffs, as well as dataset-to-dataset variability.

For the best performing function prediction methods we test, we find that adding in the KEGG pathway edges using the highway approach is superior to just adding in the KEGG edges naively.

In order to compare the effectiveness of the proposed time-series preprocessing method with the other existing methods, we test them on four different voice/speech datasets using nearly twenty popular and traditional classification algorithms in data mining.

We predict the stages of the images from the evaluation set using the ensemble of different subset of methods, and the results are summarized in Table 2. Apart from the ensemble of all methods, we test three other scenarios: SVM models alone, sparse models alone and SVM models plus sparse models with logistic loss.

As publicly available domain predictors are limited, to have a more extensive benchmark with the state-of-the-art methods, we test ThreaDom on the protein targets in CASP8 (Ezkurdia et al., 2009), which is the last community-wide blind experiment on protein domain prediction (DP).

We concluded that RF was the most robust and flexible among the imputation methods we tested.

Simulation study showed that this approach is more powerful in detecting multi-locus associations than other methods we tested, including one of ours.

To add to current methods, we tested an alternative strategy for plasma protein profiling using pancreatic cancer as the model.

To document the strengths of these methods, we tested examples of respective promoters for each C. glutamicum sigma factor.

The first time derivative of the magnetic time series is the simplest and most intuitive to understand of the methods we tested.