Abstract
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.
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Schulz, M.H., Köhler, S., Bauer, S., Vingron, M., Robinson, P.N. (2009). Exact Score Distribution Computation for Similarity Searches in Ontologies. In: Salzberg, S.L., Warnow, T. (eds) Algorithms in Bioinformatics. WABI 2009. Lecture Notes in Computer Science(), vol 5724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04241-6_25
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DOI: https://doi.org/10.1007/978-3-642-04241-6_25
Publisher Name: Springer, Berlin, Heidelberg
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