title: Automated semantic annotation of rare disease cases: a case study.
abstract: BACKGROUND
  As the number of clinical reports in the peer-reviewed medical literature keeps growing, there is an increasing need for online search tools to find and analyze publications on patients with similar clinical characteristics. This problem is especially critical and challenging for rare diseases, where publications of large series are scarce. Through an applied example, we illustrate how to automatically identify new relevant cases and semantically annotate the relevant literature about patient case reports to capture the phenotype of a rare disease named cerebrotendinous xanthomatosis.
  
  
  RESULTS
  Our results confirm that it is possible to automatically identify new relevant case reports with a high precision and to annotate them with a satisfactory quality (74% F-measure). Automated annotation with an emphasis to entirely describe all phenotypic abnormalities found in a disease may facilitate curation efforts by supplying phenotype retrieval and assessment of their frequency. Availability and Supplementary information: http://www.usc.es/keam/Phenotype Annotation/. Database URL: http://www.usc.es/keam/PhenotypeAnnotation/
authors.affiliation: Department of Electronics & Computer Science, Department of Applied Physics, Campus Vida, University of Santiago de Compostela, Department of Neurology, University Hospital Clinico of Santiago de Compostela and Fundación Pública Galega de Medicina Xenómica-Instituto de Investigación Sanitaria de Santiago (IDIS) and Centro de Investigación Biomédica en red de Enfermedades Raras (CIBERER), Santiago de Compostela, Spain maria.taboada@usc.es.
authors.forename: Maria
authors.initials: M
authors.lastname: Taboada
authors.affiliation: Department of Electronics & Computer Science, Department of Applied Physics, Campus Vida, University of Santiago de Compostela, Department of Neurology, University Hospital Clinico of Santiago de Compostela and Fundación Pública Galega de Medicina Xenómica-Instituto de Investigación Sanitaria de Santiago (IDIS) and Centro de Investigación Biomédica en red de Enfermedades Raras (CIBERER), Santiago de Compostela, Spain.
authors.forename: Hadriana
authors.initials: H
authors.lastname: Rodríguez
authors.affiliation: Department of Electronics & Computer Science, Department of Applied Physics, Campus Vida, University of Santiago de Compostela, Department of Neurology, University Hospital Clinico of Santiago de Compostela and Fundación Pública Galega de Medicina Xenómica-Instituto de Investigación Sanitaria de Santiago (IDIS) and Centro de Investigación Biomédica en red de Enfermedades Raras (CIBERER), Santiago de Compostela, Spain.
authors.forename: Diego
authors.initials: D
authors.lastname: Martínez
authors.affiliation: Department of Electronics & Computer Science, Department of Applied Physics, Campus Vida, University of Santiago de Compostela, Department of Neurology, University Hospital Clinico of Santiago de Compostela and Fundación Pública Galega de Medicina Xenómica-Instituto de Investigación Sanitaria de Santiago (IDIS) and Centro de Investigación Biomédica en red de Enfermedades Raras (CIBERER), Santiago de Compostela, Spain.
authors.forename: María
authors.initials: M
authors.lastname: Pardo
authors.affiliation: Department of Electronics & Computer Science, Department of Applied Physics, Campus Vida, University of Santiago de Compostela, Department of Neurology, University Hospital Clinico of Santiago de Compostela and Fundación Pública Galega de Medicina Xenómica-Instituto de Investigación Sanitaria de Santiago (IDIS) and Centro de Investigación Biomédica en red de Enfermedades Raras (CIBERER), Santiago de Compostela, Spain.
authors.forename: María Jesús
authors.initials: MJ
authors.lastname: Sobrido
mesh_terms: D001331:Automation; D064229:Biological Ontologies; D057225:Data Mining; D006801:Humans; D039781:PubMed; D035583:Rare Diseases / Q000473:pathology*; D012660:Semantics