de.mpg.escidoc.pubman.appbase.FacesBean
Deutsch
 
Hilfe Wegweiser Datenschutzhinweis Impressum Kontakt
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Buchkapitel

Exact Score Distribution Computation for Similarity Searches in Ontologies.

MPG-Autoren

Schulz,  Marcel H.
Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50613

Vingron,  Martin
Gene regulation (Martin Vingron), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50496

Robinson,  Peter N.
Research Group Development & Disease (Head: Stefan Mundlos), Max Planck Institute for Molecular Genetics, Max Planck Society;

Externe Ressourcen
Es sind keine Externen Ressourcen verfügbar
Volltexte (frei zugänglich)
Es sind keine frei zugänglichen Volltexte verfügbar
Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Schulz, M. H., Köhler, S., Bauer, S., Vingron, M., & Robinson, P. N. (2009). Exact Score Distribution Computation for Similarity Searches in Ontologies. In S. L. Salzberg, & T. Warnow (Eds.), Algorithms in Bioinformatics (pp. 298-309). New York [et al]: Springer.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0010-7D09-5
Zusammenfassung
Semantic similarity searches in ontologies are an important component of many bioinformatic algorithms, e.g., protein function prediction with the Gene Ontology. In this paper we consider the exact computation of score distributions for similarity searches in ontologies, and introduce a simple null hypothesis which can be used to compute a P-value for the statistical significance of similarity scores. We concentrate on measures based on Resnik’s definition of ontological similarity. A new algorithm is proposed that collapses subgraphs of the ontology graph and thereby allows fast score distribution computation. The new algorithm is several orders of magnitude faster than the naive approach, as we demonstrate by computing score distributions for similarity searches in the Human Phenotype Ontology.