title: Improving protein function prediction methods with integrated literature data.
abstract: BACKGROUND
  Determining the function of uncharacterized proteins is a major challenge in the post-genomic era due to the problem's complexity and scale. Identifying a protein's function contributes to an understanding of its role in the involved pathways, its suitability as a drug target, and its potential for protein modifications. Several graph-theoretic approaches predict unidentified functions of proteins by using the functional annotations of better-characterized proteins in protein-protein interaction networks. We systematically consider the use of literature co-occurrence data, introduce a new method for quantifying the reliability of co-occurrence and test how performance differs across species. We also quantify changes in performance as the prediction algorithms annotate with increased specificity.
  
  
  RESULTS
  We find that including information on the co-occurrence of proteins within an abstract greatly boosts performance in the Functional Flow graph-theoretic function prediction algorithm in yeast, fly and worm. This increase in performance is not simply due to the presence of additional edges since supplementing protein-protein interactions with co-occurrence data outperforms supplementing with a comparably-sized genetic interaction dataset. Through the combination of protein-protein interactions and co-occurrence data, the neighborhood around unknown proteins is quickly connected to well-characterized nodes which global prediction algorithms can exploit. Our method for quantifying co-occurrence reliability shows superior performance to the other methods, particularly at threshold values around 10% which yield the best trade off between coverage and accuracy. In contrast, the traditional way of asserting co-occurrence when at least one abstract mentions both proteins proves to be the worst method for generating co-occurrence data, introducing too many false positives. Annotating the functions with greater specificity is harder, but co-occurrence data still proves beneficial.
  
  
  CONCLUSIONS
  Co-occurrence data is a valuable supplemental source for graph-theoretic function prediction algorithms. A rapidly growing literature corpus ensures that co-occurrence data is a readily-available resource for nearly every studied organism, particularly those with small protein interaction databases. Though arguably biased toward known genes, co-occurrence data provides critical additional links to well-studied regions in the interaction network that graph-theoretic function prediction algorithms can exploit.
authors.affiliation: Department of Pharmacology, University of Colorado at Denver and Health Sciences Center, MS 8303, RC-1 South, 12801 East 17th Avenue, L18-6101, PO Box 6511, Aurora, CO 80045, USA. gabow@cbio.mskcc.org
authors.forename: Aaron P
authors.initials: AP
authors.lastname: Gabow
authors.forename: Sonia M
authors.initials: SM
authors.lastname: Leach
authors.forename: William A
authors.initials: WA
authors.lastname: Baumgartner
authors.forename: Lawrence E
authors.initials: LE
authors.lastname: Hunter
authors.forename: Debra S
authors.initials: DS
authors.lastname: Goldberg
mesh_terms: D016206:Databases, Bibliographic; D016247:Information Storage and Retrieval / Q000379:methods*; D009323:Natural Language Processing; D010506:Periodicals as Topic; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000145:classification* / Q000378:metabolism*; D012680:Sensitivity and Specificity; D018511:Systems Integration
chemical_list: D011506:Proteins