We observe that when the number of datasets is small, the difference in the execution times for the two systems is insignificant, but when the retrieved data are moderately large, SemLinker significantly outperforms the BDI Ontology system.
The number of overlapping SNPs between the two datasets is small for both ER-positive and overall breast cancer analyses.
The concordance between up-regulated probesets identified in the bulk and LCM datasets was smaller yet (~50%).
Note that with only 2000 SNPs, corr PEBV, GEBV) estimated from the test datasets are smaller than those presented in Table 5.
Because these datasets are smaller than the combined dataset for all lineages, we did not attempt to jointly infer the values of the mutation rate and the mean tract length of imported sequence fragments.
The tree length values for the most of the land plant datasets were smaller than optimal (< 1; Table 2 and Additional file 2), hence the number of cases with detected positive selection in land plants may be underestimated.
The accuracy obtained in the second dataset is smaller than the first one.
Also, audio fingerprints result in efficient searching since the dataset is smaller.
Since the shale dataset is smaller than that of the mouse brain, Group Communication introduces much less overhead.
Residual distance of the reconstructed data in the CinC multi parameter dataset is smaller than that in the two-channel arrhythmia dataset.
