Abstract
A great deal of information on the molecular genetics and biochemistry of model organisms has been reported in the scientific literature. However, this data is typically described in free text form and is not readily amenable to computational analyses. To this end, the BioGRID database systematically curates the biomedical literature for genetic and protein interaction data. This data is provided in a standardized computationally tractable format and includes structured annotation of experimental evidence. BioGRID curation necessarily involves substantial human effort by expert curators who must read each publication to extract the relevant information. Computational text-mining methods offer the potential to augment and accelerate manual curation. To facilitate the development of practical text-mining strategies, a new challenge was organized in BioCreative V for the BioC task, the collaborative Biocurator Assistant Task. This was a non-competitive, cooperative task in which the participants worked together to build BioC-compatible modules into an integrated pipeline to assist BioGRID curators. As an integral part of this task, a test collection of full text articles was developed that contained both biological entity annotations (gene/protein and organism/species) and molecular interaction annotations (protein–protein and genetic interactions (PPIs and GIs)). This collection, which we call the BioC-BioGRID corpus, was annotated by four BioGRID curators over three rounds of annotation and contains 120 full text articles curated in a dataset representing two major model organisms, namely budding yeast and human. The BioC-BioGRID corpus contains annotations for 6409 mentions of genes and their Entrez Gene IDs, 186 mentions of organism names and their NCBI Taxonomy IDs, 1867 mentions of PPIs and 701 annotations of PPI experimental evidence statements, 856 mentions of GIs and 399 annotations of GI evidence statements. The purpose, characteristics and possible future uses of the BioC-BioGRID corpus are detailed in this report.Database URL: http://bioc.sourceforge.net/BioC-BioGRID.html
Highlights
BioCreative (Critical Assessment of Information Extraction in Biology) [1,2,3,4] is a collaborative initiative to provide a common evaluation framework for monitoring and assessing the state-of-the-art of text-mining systems applied to biologically relevant problems
The goal of the BioCreative challenges has been to pose tasks that will result in systems capable of scaling for use by general biology researchers and more specialized end users such as database curators
The task was positioned as a collaboration rather than a competition such that participating teams created complementary modules that could be seamlessly integrated into a system capable of assisting BioGRID curators
Summary
BioCreative (Critical Assessment of Information Extraction in Biology) [1,2,3,4] is a collaborative initiative to provide a common evaluation framework for monitoring and assessing the state-of-the-art of text-mining systems applied to biologically relevant problems. An important contribution of BioCreative challenges has been the generation of shared gold standard datasets, prepared by domain experts, for the training and testing of text-mining applications. These collections, and the associated evaluation methods, represent an important resource for continued development and improvement of text-mining applications. The resulting interactive system triaged sentences from full text articles in order to identify text passages associated with protein–protein and genetic interactions (abbreviated PPI and GI, respectively). These sentences were highlighted in the biocurator assistant viewer [13]. World-wide, developed one or more modules independently [13,14,15,16,17,18], integrated via BioC, to insure the interoperability of the different systems [11]
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