Representation of probabilistic scientific knowledge

Abstract

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 https://github.com/larisa-soldatova/HELO.

Figure 1

An example of the HELO representation of a research statement. The figure shows the representation of the values of the prior and posterior probabilities of the research statement about sirtuins, and also the supporting and refuting evidence.

Figure 2

An overview of the ontology HELO. The figure shows the top-level classes of HELO and some of their extentions.

Figure 3

The probabilities that the selected compounds have high GI₅₀