title: How to link ontologies and protein-protein interactions to literature: text-mining approaches and the BioCreative experience.
abstract: There is an increasing interest in developing ontologies and controlled vocabularies to improve the efficiency and consistency of manual literature curation, to enable more formal biocuration workflow results and ultimately to improve analysis of biological data. Two ontologies that have been successfully used for this purpose are the Gene Ontology (GO) for annotating aspects of gene products and the Molecular Interaction ontology (PSI-MI) used by databases that archive protein-protein interactions. The examination of protein interactions has proven to be extremely promising for the understanding of cellular processes. Manual mapping of information from the biomedical literature to bio-ontology terms is one of the most challenging components in the curation pipeline. It requires that expert curators interpret the natural language descriptions contained in articles and infer their semantic equivalents in the ontology (controlled vocabulary). Since manual curation is a time-consuming process, there is strong motivation to implement text-mining techniques to automatically extract annotations from free text. A range of text mining strategies has been devised to assist in the automated extraction of biological data. These strategies either recognize technical terms used recurrently in the literature and propose them as candidates for inclusion in ontologies, or retrieve passages that serve as evidential support for annotating an ontology term, e.g. from the PSI-MI or GO controlled vocabularies. Here, we provide a general overview of current text-mining methods to automatically extract annotations of GO and PSI-MI ontology terms in the context of the BioCreative (Critical Assessment of Information Extraction Systems in Biology) challenge. Special emphasis is given to protein-protein interaction data and PSI-MI terms referring to interaction detection methods.
authors.affiliation: Structural and Computational Biology Group, Spanish National Cancer Research Centre (CNIO), Spain.
authors.forename: Martin
authors.initials: M
authors.lastname: Krallinger
authors.forename: Florian
authors.initials: F
authors.lastname: Leitner
authors.forename: Miguel
authors.initials: M
authors.lastname: Vazquez
authors.forename: David
authors.initials: D
authors.lastname: Salgado
authors.forename: Christophe
authors.initials: C
authors.lastname: Marcelle
authors.forename: Mike
authors.initials: M
authors.lastname: Tyers
authors.forename: Alfonso
authors.initials: A
authors.lastname: Valencia
authors.forename: Andrew
authors.initials: A
authors.lastname: Chatr-aryamontri
mesh_terms: D057225:Data Mining / Q000379:methods*; D030562:Databases, Protein; D058977:Molecular Sequence Annotation; D009323:Natural Language Processing; D025941:Protein Interaction Mapping; D040901:Proteomics / Q000379:methods*; D018875:Vocabulary, Controlled