title: Propagating semantic information in biochemical network models.
abstract: BACKGROUND
  To enable automatic searches, alignments, and model combination, the elements of systems biology models need to be compared and matched across models. Elements can be identified by machine-readable biological annotations, but assigning such annotations and matching non-annotated elements is tedious work and calls for automation.
  
  
  RESULTS
  A new method called "semantic propagation" allows the comparison of model elements based not only on their own annotations, but also on annotations of surrounding elements in the network. One may either propagate feature vectors, describing the annotations of individual elements, or quantitative similarities between elements from different models. Based on semantic propagation, we align partially annotated models and find annotations for non-annotated model elements.
  
  
  CONCLUSIONS
  Semantic propagation and model alignment are included in the open-source library semanticSBML, available on sourceforge. Online services for model alignment and for annotation prediction can be used at http://www.semanticsbml.org.
authors.affiliation: Institut für Biologie, Theoretische Biophysik, Humboldt-Universität zu Berlin, Invalidenstr 42, 10115 Berlin, Germany. marvin.schulz@biologie.hu-berlin.de
authors.forename: Marvin
authors.initials: M
authors.lastname: Schulz
authors.forename: Edda
authors.initials: E
authors.lastname: Klipp
authors.forename: Wolfram
authors.initials: W
authors.lastname: Liebermeister
mesh_terms: D006801:Humans; D008954:Models, Biological; D060066:Protein Interaction Maps; D012660:Semantics; D012984:Software; D049490:Systems Biology / Q000379:methods*