Anyway, there you go: Evidence, if it were needed, that Scientologists make terrible stand-up comedians.
GO annotations from all GO evidence codes are included in the dataset, but annotations with qualifiers (e.g. NOT, colocalizes-with) are excluded.
We do this with respect to complete GO annotation data, independent of GO evidence code.
Specifically, we proposed two challenge tasks, aimed toward automated GO concept recognition from full-length articles: GO evidence text is critical for human curators to make associated GO annotations.
In addition to assigning GO terms, JCVI's automated methods assign the GO evidence codes to distinguish between functional annotations made based on experimental data or solely on computational evidence.
To efficiently and precisely retrieve GO information from large amount of biomedical resources, we propose a GO evidence sentence retrieval system conducted via combinatorial applications of semantic class and rule patterns to automatically retrieve GO evidence sentences with specific gene mentions from full-length articles.
To avoid potential circularity we filtered the GO-gene associations in the training set by GO evidence codes.
With a focus on gene functions, it includes two subtasks: (i) retrieving GO evidence sentences for relevant genes and (ii) predicting GO terms for relevant genes.
The DbTF curation guidelines presented in the current work use one of the following GO evidence codes: Inferred from Direct Assay (IDA), Inferred from Physical Interaction (IPI), Inferred from Mutant Phenotype (IMP) or Inferred by Curator (IC).
InterPro2GO annotations all apply the GO evidence code 'Inferred from Electronic Annotation' (IEA), indicating that the GO annotations are the result of an automated prediction pipeline and have not been individually reviewed by curators.
These sentences describe experimental results and can be used to make a complete GO annotation (i.e. the entity being annotated, GO term and GO evidence code).
