Table 1

Members of the UAG represent a diverse sample of end users with multiple text mining needs

Domains represented by UAG members and Chair*


Model Organism Databases

dictyBase, MGI, TAIR, Gramene, Wormbase


Protein Sequence Databases

UniProtKB


Protein-Protein Interaction Databases

BioGrid, MINT


Ontologies

Gene Ontology, Protein Ontology, Plant Ontology, Microbial Phenotype Ontology


Pharmaceutical Companies

Dupont, Merck KGaA, Pfizer


Examples of text mining needs among UAG members


□ gene normalization

□ mapping to ontologies (e.g., GO, PO, PRO) either for annotation or semantic integration

□ entity normalization and relevance scoring to help automate relationship extraction and data integration of text mined facts with external and internal sources

Identification of articles:

□ related to a specific topic (PPI, biomarkers)

□ reporting experimental information for gene/proteins in a given organism

□ with experimental characterization of gene/protein with associated reporting of organism and gene normalization when available

□ new articles not yet in the database


*Note that some members represent more than one resource

Arighi et al. BMC Bioinformatics 2011 12(Suppl 8):S4   doi:10.1186/1471-2105-12-S8-S4

Open Data