title: Towards an Age-Phenome Knowledge-base.
abstract: BACKGROUND
  Currently, data about age-phenotype associations are not systematically organized and cannot be studied methodically. Searching for scientific articles describing phenotypic changes reported as occurring at a given age is not possible for most ages.
  
  
  RESULTS
  Here we present the Age-Phenome Knowledge-base (APK), in which knowledge about age-related phenotypic patterns and events can be modeled and stored for retrieval. The APK contains evidence connecting specific ages or age groups with phenotypes, such as disease and clinical traits. Using a simple text mining tool developed for this purpose, we extracted instances of age-phenotype associations from journal abstracts related to non-insulin-dependent Diabetes Mellitus. In addition, links between age and phenotype were extracted from clinical data obtained from the NHANES III survey. The knowledge stored in the APK is made available for the relevant research community in the form of 'Age-Cards', each card holds the collection of all the information stored in the APK about a particular age. These Age-Cards are presented in a wiki, allowing community review, amendment and contribution of additional information. In addition to the wiki interaction, complex searches can also be conducted which require the user to have some knowledge of database query construction.
  
  
  CONCLUSIONS
  The combination of a knowledge model based repository with community participation in the evolution and refinement of the knowledge-base makes the APK a useful and valuable environment for collecting and curating existing knowledge of the connections between age and phenotypes.
authors.affiliation: Shraga Segal Department of Microbiology and Immunology, The National Institute for Biotechnology in the Negev, Ben Gurion University, Israel.
authors.forename: Nophar
authors.initials: N
authors.lastname: Geifman
authors.forename: Eitan
authors.initials: E
authors.lastname: Rubin
mesh_terms: D000367:Age Factors; D057225:Data Mining; D006801:Humans; D019359:Knowledge; D010641:Phenotype