ArticleBMC bioinformatics2011
Exact score distribution computation for ontological similarity searches.
Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Prenatal phenotyping: A community effort to enhance the Human Phenotype Ontology.American journal of medical genetics. Part C, Seminars in medical genetics · 2022Article
- Modeling seizures in the Human Phenotype Ontology according to contemporary ILAE concepts makes big phenotypic data tractable.Epilepsia · 2021Article
- Strategies to Uplift Novel Mendelian Gene Discovery for Improved Clinical Outcomes.Frontiers in genetics · 2021Review
- The case for open science: rare diseases.JAMIA open · 2020Review
- Encoding Clinical Data with the Human Phenotype Ontology for Computational Differential Diagnostics.Current protocols in human genetics · 2019Article
- [From symptom to syndrome using modern software support].Der Internist · 2018Review
- Improved ontology-based similarity calculations using a study-wise annotation model.Database : the journal of biological databases and curation · 2018Article
- Exploring Approaches for Detecting Protein Functional Similarity within an Orthology-based Framework.Scientific reports · 2017Article
- Evaluating the effect of annotation size on measures of semantic similarity.Journal of biomedical semantics · 2017Article
- Phenotype-driven strategies for exome prioritization of human Mendelian disease genes.Genome medicine · 2015Review
- Bayesian ontology querying for accurate and noise-tolerant semantic searches.Bioinformatics (Oxford, England) · 2012Article
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4 authors.
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Abstract
backgroundSemantic similarity searches in ontologies are an important component of many bioinformatic algorithms, e.g., finding functionally related proteins with the Gene Ontology or phenotypically similar diseases with the Human Phenotype Ontology (HPO). We have recently shown that the performance of semantic similarity searches can be improved by ranking results according to the probability of obtaining a given score at random rather than by the scores themselves. However, to date, there are no algorithms for computing the exact distribution of semantic similarity scores, which is necessary for computing the exact P-value of a given score.
resultsIn 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 HPO. It is shown that exact P-value calculation improves clinical diagnosis using the HPO compared to approaches based on sampling.
conclusionsThe new algorithm enables for the first time exact P-value calculation via exact score distribution computation for ontology similarity searches. The approach is applicable to any ontology for which the annotation-propagation rule holds and can improve any bioinformatic method that makes only use of the raw similarity scores. The algorithm was implemented in Java, supports any ontology in OBO format, and is available for non-commercial and academic usage under: https://compbio.charite.de/svn/hpo/trunk/src/tools/significance/
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.