For the TAIR evaluation, we instead compared the time spent manually curating the gold-standard set of annotations with the time spent annotating the same papers using Textpresso and found an ∼10-fold decrease in the amount of time spent in annotating when using Textpresso.
In this study a classifier was trained using Gene Ontology as a gold-standard set of annotations and validated with an independent dataset of localization annotations.
Third, a standard set of Representative Proteomes (RPs) would enable the scientific community to make direct comparison between clusters and annotations produced by various methods.
To evaluate the performance of Textpresso for CCC at dictyBase and TAIR, we compared a gold-standard set of sentences and annotations with those derived from Textpresso searches.
Then, for arrays with matched RNA-Seq data, we describe a technique for adjusting the marginal FPR by using the RNA-Seq data as the gold standard set instead of the annotation.
In 143 cases (58%), the network-based procedure extracts GO terms neglected by the standard method, and in 86 cases (35%), some of the newly enriched GO terms are not included in the set of annotations characterizing the input proteins.
The set of annotations constitutes a complete lattice.
The set of annotations should correspond to physically consistent locations on the human face.
We randomized the original set of annotations 1000 times leading to 2002 phenotype annotation sets in total.
