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This article is part of the supplement: Proceedings of the Bio-Ontologies Special Interest Group 2012

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Representation of probabilistic scientific knowledge

Larisa N Soldatova1*, Andrey Rzhetsky2, Kurt De Grave3 and Ross D King4

Author Affiliations

1 Department of Information Systems and Computing, Brunel University, London, UK

2 Department of Medicine & Department of Human Genetics, the University of Chicago, USA

3 Department of Computer Science, KU Leuven, Belgium

4 Manchester Institute of Biotechnology, the University of Manchester, UK

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Journal of Biomedical Semantics 2013, 4(Suppl 1):S7  doi:10.1186/2041-1480-4-S1-S7

Published: 15 April 2013


The theory of probability is widely used in biomedical research for data analysis and modelling. In previous work the probabilities of the research hypotheses have been recorded as experimental metadata. The ontology HELO is designed to support probabilistic reasoning, and provides semantic descriptors for reporting on research that involves operations with probabilities. HELO explicitly links research statements such as hypotheses, models, laws, conclusions, etc. to the associated probabilities of these statements being true. HELO enables the explicit semantic representation and accurate recording of probabilities in hypotheses, as well as the inference methods used to generate and update those hypotheses. We demonstrate the utility of HELO on three worked examples: changes in the probability of the hypothesis that sirtuins regulate human life span; changes in the probability of hypotheses about gene functions in the S. cerevisiae aromatic amino acid pathway; and the use of active learning in drug design (quantitative structure activity relation learning), where a strategy for the selection of compounds with the highest probability of improving on the best known compound was used. HELO is open source and available at webcite

ontology; knowledge representation; probabilistic reasoning