title: k-Information gain scaled nearest neighbors: a novel approach to classifying protein-protein interaction-related documents.
abstract: Although publicly accessible databases containing protein-protein interaction (PPI)-related information are important resources to bench and in silico research scientists alike, the amount of time and effort required to keep them up to date is often burdonsome. In an effort to help identify relevant PPI publications, text-mining tools, from the machine learning discipline, can be applied to help in this process. Here, we describe and evaluate two document classification algorithms that we submitted to the BioCreative II.5 PPI Classification Challenge Task. This task asked participants to design classifiers for identifying documents containing PPI-related information in the primary literature, and evaluated them against one another. One of our systems was the overall best-performing system submitted to the challenge task. It utilizes a novel approach to k-nearest neighbor classification, which we describe here, and compare its performance to those of two support vector machine-based classification systems, one of which was also evaluated in the challenge task.
authors.affiliation: Oregon Health & Science University, Portland.
authors.forename: Kyle H
authors.initials: KH
authors.lastname: Ambert
authors.forename: Aaron M
authors.initials: AM
authors.lastname: Cohen
mesh_terms: D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D030562:Databases, Protein; D060066:Protein Interaction Maps; D015203:Reproducibility of Results; D060388:Support Vector Machine