Improving protein function prediction methods with integrated literature data

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. We find that including information on the co-occurrence of proteins within an abstract greatly boosts performance in the Functional Flow graph-theoretic function predicti...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Aaron Gabow, Sonia M. Leach, William A. Baumgartner Jr., Lawrence Hunter, Debra Goldberg
• Keywords: BMCBI 2008, Co-occurrence, Co-occurrence Data, Proteins