title: An improved method for scoring protein-protein interactions using semantic similarity within the gene ontology.
abstract: BACKGROUND
  Semantic similarity measures are useful to assess the physiological relevance of protein-protein interactions (PPIs). They quantify similarity between proteins based on their function using annotation systems like the Gene Ontology (GO). Proteins that interact in the cell are likely to be in similar locations or involved in similar biological processes compared to proteins that do not interact. Thus the more semantically similar the gene function annotations are among the interacting proteins, more likely the interaction is physiologically relevant. However, most semantic similarity measures used for PPI confidence assessment do not consider the unequal depth of term hierarchies in different classes of cellular location, molecular function, and biological process ontologies of GO and thus may over-or under-estimate similarity.
  
  
  RESULTS
  We describe an improved algorithm, Topological Clustering Semantic Similarity (TCSS), to compute semantic similarity between GO terms annotated to proteins in interaction datasets. Our algorithm, considers unequal depth of biological knowledge representation in different branches of the GO graph. The central idea is to divide the GO graph into sub-graphs and score PPIs higher if participating proteins belong to the same sub-graph as compared to if they belong to different sub-graphs.
  
  
  CONCLUSIONS
  The TCSS algorithm performs better than other semantic similarity measurement techniques that we evaluated in terms of their performance on distinguishing true from false protein interactions, and correlation with gene expression and protein families. We show an average improvement of 4.6 times the F1 score over Resnik, the next best method, on our Saccharomyces cerevisiae PPI dataset and 2 times on our Homo sapiens PPI dataset using cellular component, biological process and molecular function GO annotations.
authors.affiliation: Department of Computer Science, University of Toronto, 10 Kings College Road, Toronto, Ontario M5S3G4, Canada.
authors.forename: Shobhit
authors.initials: S
authors.lastname: Jain
authors.forename: Gary D
authors.initials: GD
authors.lastname: Bader
mesh_terms: D000465:Algorithms; D030562:Databases, Protein; D006801:Humans; D058977:Molecular Sequence Annotation; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000737:chemistry / Q000235:genetics / Q000378:metabolism*; D012660:Semantics
chemical_list: D011506:Proteins