Based on this benchmark, we tested the other three filters: multiplicity, accessibility and Degradome-seq support.
Using the well-characterized proteome from Tribolium castaneum as a benchmark, we tested if similar numbers of proteins could be predicted in our assemblies.
On a comprehensive benchmark, we test our method based on three sets of experimental data: high-resolution X-ray crystallography, structures from neutron diffraction, and NOE proton-proton distance restraints.
Using the MediaBench benchmark suite, we tested our methodology and architecture to accelerate software.
Accordingly, on every group of benchmark data, we tested FFP, CVTree and UA with different parameter values and used those which produced the best results on this group.
As a benchmark, we also tested the performance of several standard machine learning methods that have previously been used for ADR prediction.
As a benchmark, we also tested a simpler empirical approach (PEAKVI) that relates yield to the peak value of a time series of spatially aggregated vegetation indices, similar to methods used in current operational monitoring.
As a benchmark, we first tested the most commonly used Drosophila targeting vector (pGX) and protocol (Huang et al., 2009) with rhogap102A (a gene located on the 4th chromosome of Drosophila).
We tested our benchmark on five methods, three of which use sequence conservation to improve prediction accuracy.
Hence, we test the benchmark functions on BH and BA in this study.
