title: Disambiguating proteins, genes, and RNA in text: a machine learning approach.
abstract: We present an automated system for assigning protein, gene, or mRNA class labels to biological terms in free text. Three machine learning algorithms and several extended ways for defining contextual features for disambiguation are examined, and a fully unsupervised manner for obtaining training examples is proposed. We train and evaluate our system over a collection of 9 million words of molecular biology journal articles, obtaining accuracy rates up to 85%.
authors.affiliation: Department of Computer Science, Columbia University, 1214 Amsterdam Avenue, New York, NY 10027, USA. vh@cs.columbia.edu
authors.forename: V
authors.initials: V
authors.lastname: Hatzivassiloglou
authors.forename: P A
authors.initials: PA
authors.lastname: Duboué
authors.forename: A
authors.initials: A
authors.lastname: Rzhetsky
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D019295:Computational Biology; D003625:Data Collection; D005796:Genes; D009323:Natural Language Processing; D011506:Proteins; D012313:RNA
chemical_list: D011506:Proteins; D012313:RNA