title: Multi-label literature classification based on the Gene Ontology graph.
abstract: BACKGROUND
  The Gene Ontology is a controlled vocabulary for representing knowledge related to genes and proteins in a computable form. The current effort of manually annotating proteins with the Gene Ontology is outpaced by the rate of accumulation of biomedical knowledge in literature, which urges the development of text mining approaches to facilitate the process by automatically extracting the Gene Ontology annotation from literature. The task is usually cast as a text classification problem, and contemporary methods are confronted with unbalanced training data and the difficulties associated with multi-label classification.
  
  
  RESULTS
  In this research, we investigated the methods of enhancing automatic multi-label classification of biomedical literature by utilizing the structure of the Gene Ontology graph. We have studied three graph-based multi-label classification algorithms, including a novel stochastic algorithm and two top-down hierarchical classification methods for multi-label literature classification. We systematically evaluated and compared these graph-based classification algorithms to a conventional flat multi-label algorithm. The results indicate that, through utilizing the information from the structure of the Gene Ontology graph, the graph-based multi-label classification methods can significantly improve predictions of the Gene Ontology terms implied by the analyzed text. Furthermore, the graph-based multi-label classifiers are capable of suggesting Gene Ontology annotations (to curators) that are closely related to the true annotations even if they fail to predict the true ones directly. A software package implementing the studied algorithms is available for the research community.
  
  
  CONCLUSIONS
  Through utilizing the information from the structure of the Gene Ontology graph, the graph-based multi-label classification methods have better potential than the conventional flat multi-label classification approach to facilitate protein annotation based on the literature.
authors.affiliation: Department of Biostatistics, Bioinformatics and Epidemiology, Medical University of South Carolina, Charleston, SC 29425, USA. jinbo@musc.edu
authors.forename: Bo
authors.initials: B
authors.lastname: Jin
authors.forename: Brian
authors.initials: B
authors.lastname: Muller
authors.forename: Chengxiang
authors.initials: C
authors.lastname: Zhai
authors.forename: Xinghua
authors.initials: X
authors.lastname: Lu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D023281:Genomics / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D020407:Internet; D039781:PubMed; D015203:Reproducibility of Results; D012660:Semantics; D012984:Software; D018875:Vocabulary, Controlled