- funded by IMI-JU
- 01 March 2011 - 31 August 2016
- 11 EFPIA members
- 10 Academia and 5 SME's
- Total cost € 23,032,609
ProbOnto is a knowledge base and ontology of probability distributions.
It was designed to facilitate the encoding of nonlinear-mixed effect models and their annotation in PharmML developed by DDMoRe.
PharmML interface is provided in form of a generic schema for the definition of the distributions and their parameters. Defining function can be accessed via methods provided in the PharmML schema.
The scope and features of the database made ProbOnto very valuable in encoding of diverse models applicable to discrete (e.g. count, categorical and time-to-event) and continuous data. See use examples and detailed specification on probonto.org
Version 2.0 of ProbOnto contains over 100 uni- and multivariate distributions and alternative parameterizations, over 180 relationships and re-parameterization formulas ad supports encoding of univariate mixture distributions.
The knowledge base stores for each distribution:
The knowledge base built from a simple ontological model. At its core, a probability distribution is an instance of the class thereof, a specialization of the class of mathematical objects. A distribution relates to a number of other individuals, which are instances of various categories in the ontology. For example, these are parameters and related functions associated with a given probability distribution. This strategy allows for the rich representation of attributes and relationships between domain objects. The ontology can be seen as a conceptual schema in the domain of mathematics and has been implemented as a PowerLoom knowledge base. An OWL version is generated programmatically using the Jena API.
Output for ProbOnto are provided as supplementary materials and published on or linked from the probonto.org website. The OWL version of ProbOnto is available via Ontology Lookup Service (OLS) to facilitate simple searching and visualization of the content. In addition the OLS API provides methods to programmatically access ProbOnto and to integrate it into applications.
Reference: Swat MJ, Grenon P, Wimalaratne SM., ProbOnto - Ontology and Knowledge Base of Probability Distributions. Bioinformatics (2016) 32 (17):2719-2721. doi:10.1093/bioinformatics/btw170