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Text mining is a flexible technology that can be applied to numerous different tasks in biology and medicine. We present a system for extracting disease-gene associations from biomedical abstracts. The system consists of a highly efficient dictionary-based tagger for named entity recognition of human genes and diseases, which we combine with a scoring(More)
Information on protein subcellular localization is important to understand the cellular functions of proteins. Currently, such information is manually curated from the literature, obtained from high-throughput microscopy-based screens and predicted from primary sequence. To get a comprehensive view of the localization of a protein, it is thus necessary to(More)
The current version of the Human Disease Ontology (DO) (http://www.disease-ontology.org) database expands the utility of the ontology for the examination and comparison of genetic variation, phenotype, protein, drug and epitope data through the lens of human disease. DO is a biomedical resource of standardized common and rare disease concepts with stable(More)
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