title: Using ontology-based annotation to profile disease research.
abstract: BACKGROUND
  Profiling the allocation and trend of research activity is of interest to funding agencies, administrators, and researchers. However, the lack of a common classification system hinders the comprehensive and systematic profiling of research activities. This study introduces ontology-based annotation as a method to overcome this difficulty. Analyzing over a decade of funding data and publication data, the trends of disease research are profiled across topics, across institutions, and over time.
  
  
  RESULTS
  This study introduces and explores the notions of research sponsorship and allocation and shows that leaders of research activity can be identified within specific disease areas of interest, such as those with high mortality or high sponsorship. The funding profiles of disease topics readily cluster themselves in agreement with the ontology hierarchy and closely mirror the funding agency priorities. Finally, four temporal trends are identified among research topics.
  
  
  CONCLUSIONS
  This work utilizes disease ontology (DO)-based annotation to profile effectively the landscape of biomedical research activity. By using DO in this manner a use-case driven mechanism is also proposed to evaluate the utility of classification hierarchies.
authors.affiliation: Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California 94305, USA. liuyipei@stanford.edu
authors.forename: Yi
authors.initials: Y
authors.lastname: Liu
authors.forename: Adrien
authors.initials: A
authors.lastname: Coulet
authors.forename: Paea
authors.initials: P
authors.lastname: LePendu
authors.forename: Nigam H
authors.initials: NH
authors.lastname: Shah
mesh_terms: D015706:Bibliometrics; D035843:Biomedical Research / Q000145:classification* / Q000706:statistics & numerical data; D004194:Disease / Q000145:classification; D006801:Humans; D010506:Periodicals as Topic; D012108:Research Personnel; D012109:Research Support as Topic / Q000706:statistics & numerical data