title: Improving domain-based protein interaction prediction using biologically significant negative datasets.
abstract: We propose a domain-based classification method to predict protein-protein interactions using probabilities of putative interacting domain pairs derived from both experimentally-determined interacting protein pairs and carefully-chosen non-interacting protein pairs. Multi-species comparative results for protein interaction prediction show that such careful generation of biologically meaningful negative training data can improve classification performance.
authors.affiliation: Knowledge Discovery Department, Institute For Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613. xlli@i2r.a-star.edu.sg
authors.forename: Xiao-Li
authors.initials: XL
authors.lastname: Li
authors.forename: Soon-Heng
authors.initials: SH
authors.lastname: Tan
authors.forename: See-Kiong
authors.initials: SK
authors.lastname: Ng
mesh_terms: D000818:Animals; D030562:Databases, Protein; D015233:Models, Statistical; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000379:methods*; D017434:Protein Structure, Tertiary; D012984:Software; D013045:Species Specificity