title: Choosing negative examples for the prediction of protein-protein interactions.
abstract: The protein-protein interaction networks of even well-studied model organisms are sketchy at best, highlighting the continued need for computational methods to help direct experimentalists in the search for novel interactions. This need has prompted the development of a number of methods for predicting protein-protein interactions based on various sources of data and methodologies. The common method for choosing negative examples for training a predictor of protein-protein interactions is based on annotations of cellular localization, and the observation that pairs of proteins that have different localization patterns are unlikely to interact. While this method leads to high quality sets of non-interacting proteins, we find that this choice can lead to biased estimates of prediction accuracy, because the constraints placed on the distribution of the negative examples makes the task easier. The effects of this bias are demonstrated in the context of both sequence-based and non-sequence based features used for predicting protein-protein interactions.
authors.affiliation: Department of Computer Science, Colorado State University, Fort Collins CO, USA. asa@cs.colostate.edu
authors.forename: Asa
authors.initials: A
authors.lastname: Ben-Hur
authors.forename: William Stafford
authors.initials: WS
authors.lastname: Noble
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D008968:Molecular Conformation; D020411:Oligonucleotide Array Sequence Analysis; D010766:Phosphorylation; D017510:Protein Folding; D025941:Protein Interaction Mapping; D011506:Proteins / Q000737:chemistry; D040901:Proteomics; D012372:ROC Curve; D016415:Sequence Alignment; D012984:Software
chemical_list: D011506:Proteins