title: Large-scale directional relationship extraction and resolution.
abstract: BACKGROUND
  Relationships between entities such as genes, chemicals, metabolites, phenotypes and diseases in MEDLINE are often directional. That is, one may affect the other in a positive or negative manner. Detection of causality and direction is key in piecing pathways together and in examining possible implications of experimental results. Because of the size and growth of biomedical literature, it is increasingly important to be able to automate this process as much as possible.
  
  
  RESULTS
  Here we present a method of relation extraction using dependency graph parsing with SVM classification. We tested the SVM classifier first on gold standard corpora from GENIA and find it achieved 82% precision and 94.8% recall (F-measure of 87.9) on these standardized test sets. We then applied the entire system to all available MEDLINE abstracts for two target interactions with known effects. We find that while some directional relations are extracted with low ambiguity, others are apparently contradictory, at least when considered in an isolated context. When examined, it is apparent some are dependent upon the surrounding context (e.g. whether the relationship referred to short-term or long-term effects, or whether the focus was extracellular versus intracellular).
  
  
  CONCLUSIONS
  Thesaurus-based directional relation extraction can be done with reasonable accuracy, but is prone to false-positives on larger corpora due to noun modifiers. Furthermore, methods of resolving or disambiguating relationship context and contingencies are important for large-scale corpora.
authors.affiliation: Arthritis and Immunology Research Program, Oklahoma Medical Research Foundation, 825 N,E, 13th Street, Oklahoma City, Oklahoma 73104-5005, USA. Cory-Giles@omrf.org
authors.forename: Cory B
authors.initials: CB
authors.lastname: Giles
authors.forename: Jonathan D
authors.initials: JD
authors.lastname: Wren
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*; D025941:Protein Interaction Mapping / Q000379:methods; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
chemical_list: D020543:Proteome
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