title: PreBIND and Textomy--mining the biomedical literature for protein-protein interactions using a support vector machine.
abstract: BACKGROUND
  The majority of experimentally verified molecular interaction and biological pathway data are present in the unstructured text of biomedical journal articles where they are inaccessible to computational methods. The Biomolecular interaction network database (BIND) seeks to capture these data in a machine-readable format. We hypothesized that the formidable task-size of backfilling the database could be reduced by using Support Vector Machine technology to first locate interaction information in the literature. We present an information extraction system that was designed to locate protein-protein interaction data in the literature and present these data to curators and the public for review and entry into BIND.
  
  
  RESULTS
  Cross-validation estimated the support vector machine's test-set precision, accuracy and recall for classifying abstracts describing interaction information was 92%, 90% and 92% respectively. We estimated that the system would be able to recall up to 60% of all non-high throughput interactions present in another yeast-protein interaction database. Finally, this system was applied to a real-world curation problem and its use was found to reduce the task duration by 70% thus saving 176 days.
  
  
  CONCLUSIONS
  Machine learning methods are useful as tools to direct interaction and pathway database back-filling; however, this potential can only be realized if these techniques are coupled with human review and entry into a factual database such as BIND. The PreBIND system described here is available to the public at http://bind.ca. Current capabilities allow searching for human, mouse and yeast protein-interaction information.
authors.affiliation: Samuel Lunenfeld Research Institute, Toronto, M5G 1X5, Canada. ian.donaldson@utoronto.ca
authors.forename: Ian
authors.initials: I
authors.lastname: Donaldson
authors.forename: Joel
authors.initials: J
authors.lastname: Martin
authors.forename: Berry
authors.initials: B
authors.lastname: de Bruijn
authors.forename: Cheryl
authors.initials: C
authors.lastname: Wolting
authors.forename: Vicki
authors.initials: V
authors.lastname: Lay
authors.forename: Brigitte
authors.initials: B
authors.lastname: Tuekam
authors.forename: Shudong
authors.initials: S
authors.lastname: Zhang
authors.forename: Berivan
authors.initials: B
authors.lastname: Baskin
authors.forename: Gary D
authors.initials: GD
authors.lastname: Bader
authors.forename: Katerina
authors.initials: K
authors.lastname: Michalickova
authors.forename: Tony
authors.initials: T
authors.lastname: Pawson
authors.forename: Christopher W V
authors.initials: CW
authors.lastname: Hogue
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods / Q000706:statistics & numerical data; D016208:Databases, Factual / Q000639:trends; D030562:Databases, Protein / Q000639:trends; D016681:Genome, Fungal; D016247:Information Storage and Retrieval / Q000639:trends*; D025941:Protein Interaction Mapping / Q000145:classification / Q000379:methods* / Q000706:statistics & numerical data; D039781:PubMed / Q000145:classification; D012441:Saccharomyces cerevisiae / Q000235:genetics; D029701:Saccharomyces cerevisiae Proteins / Q000737:chemistry
chemical_list: D029701:Saccharomyces cerevisiae Proteins