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