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For the labeling test, two pools were created, one treated and one untreated, by mixing equal aliquots from each of the 4 RNA samples.
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Hence, single-label test data set was built from 16,344 single-label test compounds while multi-label test set was built from 3,332 multi-label test compounds.
Thereafter, these action classes are used to label test images.
Furthermore, out of 19,676 test compounds, 3,332 compounds were multi-label (multi-label test set) while 16,344 compounds were single-label (single-label test set).
SMM and MMM were tested on both single-label and multi-label test sets.
This test, however, requires [14C] labeled test substances, which clearly limits the scope of the test.
Additional file 2: McNemar's test result for single-label test sets.
In passing, we only partitioned the original single-label test dataset into subsets because the number of single-label test compounds were not only 5 times (or more) larger than the number of multi-label test compounds contained in the multi-label test, but were also well distributed over the 308 target proteins constituting our predefined set of class labels.
The formulae synthesize the counts of positively labeled test storylines for each band.
This gave us two sets of test datasets: A single-label test set comprising 16,344 single-label compounds, and a multi-label test set consisting of 3,332 multi-label compounds.
Furthermore, a single-label test dataset does not necessarily imply that each compound in the set is conclusively single-label.
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