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The other major database of curated gene expression studies, the Gene Expression Atlas (2), provides machine- rather than manually annotated data, resulting in reduced consistency of annotation across studies.
As a further validation, we compared the performance of this classifier and a classifier trained on manually annotated data and found nearly identical performance (Supplementary Note 5).
In addition, we demonstrate that our method also obtains very competitive results even when the amount of supervised data is cut by half, alleviating the dependency on manually annotated data.
The manually annotated data will test the exactitude of our classifier.
It offered manually annotated data that allowed participating researches to improve and evaluate their tools.
A complete description of the manually annotated data used for evaluation can be found in [31].
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To develop these algorithms, substantial cognitive labor was needed to manually annotate data and obtain sufficient training examples for temporally sparse activities such as those examined.
With time, the tool expanded with more and more ways to manually annotate data, from gradation levels like how sick a cow is for judging its milk production to matching systems like whether a dress fits a fashion brand's aesthetic.
For comparison, the mean duration of all activities as based on the manually reviewed, annotated data (ground truth) was 9.0 s (standard deviation 12.9 s, min 0.5 s, max 123.9 s, see Supplementary Table 1 for a comparison of algorithm-predicted and ground truth activity durations).
The data for protein complexes deposited in CORUM are manually annotated and data from high-throughput experiments are excluded.
Table 3 shows the agreement results for our manually annotated Ansar1 data.
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