BioCreAtIvE I, Task 2: Functional annotation of gene products
Molecular Biology accumulated substantial amounts of data concerning functions of genes and proteins.
Information relating to functional descriptions is generally extracted manually from textual data and stored
in biological databases to build up annotations for large collections of gene products. Those annotation databases
are crucial for the interpretation of large scale analysis approaches using bioinformatics or experimental techniques.
Due to the growing accumulation of functional descriptions in biomedical literature the need for text mining tools to
facilitate the extraction of such annotations is urgent. In order to make text mining tools useable in real world scenarios,
for instance to assist database curators during annotation of protein function, comparisons and evaluations of different
approaches on full text articles are needed.
The Critical Assessment for Information Extraction in Biology (BioCreAtIvE) contest consists of a community wide competition
aiming to evaluate different strategies for text mining tools, as applied to biomedical literature. We report on task two which
addressed the automatic extraction and assignment of Gene Ontology (GO) annotations of human proteins, using full text articles.
The predictions of task 2 are based on triplets of protein GO term article passage. The annotation-relevant text passages
were returned by the participants and evaluated by expert curators of the GO annotation (GOA) team at the European Institute of
Bioinformatics (EBI). Each participant could submit up to three results for each sub-task comprising task 2.
In total more than 15,000 individual results were provided by the participants. The curators evaluated in addition to the
annotation itself, whether the protein and the GO term were correctly predicted and traceable through the submitted text fragment.
Concepts provided by GO are currently the most extended set of terms used for annotating gene products, thus they were explored to
assess how effectively text mining tools are able to extract those annotations automatically. Although the obtained results are promising,
they are still far from reaching the required performance demanded by real world applications. Among the principal difficulties encountered
to address the proposed task, were the complex nature of the GO terms and protein names (the large range of variants which are used to
express proteins and especially GO terms in free text), and the lack of a standard training set. A range of very different strategies were
used to tackle this task. The dataset generated in line with the BioCreative challenge is publicly available and will allow new possibilities
for training information extraction methods in the domain of molecular biology.
A detailed description of the set up and results of the human gene annotation with Gene Ontology terms task, as well as the strategies
adopted by the participating groups has been published in the BioCreAtIvE I workshop proceedings as well as in a special issue of