title: Literature classification for semi-automated updating of biological knowledgebases.
abstract: BACKGROUND
  As the output of biological assays increase in resolution and volume, the body of specialized biological data, such as functional annotations of gene and protein sequences, enables extraction of higher-level knowledge needed for practical application in bioinformatics. Whereas common types of biological data, such as sequence data, are extensively stored in biological databases, functional annotations, such as immunological epitopes, are found primarily in semi-structured formats or free text embedded in primary scientific literature.
  
  
  RESULTS
  We defined and applied a machine learning approach for literature classification to support updating of TANTIGEN, a knowledgebase of tumor T-cell antigens. Abstracts from PubMed were downloaded and classified as either "relevant" or "irrelevant" for database update. Training and five-fold cross-validation of a k-NN classifier on 310 abstracts yielded classification accuracy of 0.95, thus showing significant value in support of data extraction from the literature.
  
  
  CONCLUSIONS
  We here propose a conceptual framework for semi-automated extraction of epitope data embedded in scientific literature using principles from text mining and machine learning. The addition of such data will aid in the transition of biological databases to knowledgebases.
authors.forename: Lars
authors.initials: L
authors.lastname: Olsen
authors.forename: Ulrich
authors.initials: U
authors.lastname: Johan Kudahl
authors.forename: Ole
authors.initials: O
authors.lastname: Winther
authors.forename: Vladimir
authors.initials: V
authors.lastname: Brusic
mesh_terms: D001185:Artificial Intelligence; D057225:Data Mining / Q000379:methods*; D018984:Epitopes, T-Lymphocyte; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D051188:Knowledge Bases; D039781:PubMed
chemical_list: D018984:Epitopes, T-Lymphocyte