Computing Confidence Measures in Stochastic Logic Programs

Stochastic logic programs (SLPs) provide an efficient representation for complex tasks such as modelling metabolic pathways. In recent years, methods have been developed to perform parameter and structure learning in SLPs. These techniques have been applied for estimating rates of enzyme-catalyzed reactions with success. However there does not exist any method that can provide statistical inferences and compute confidence in the learned SLP models. We propose a novel approach for drawing such inferences and calculating confidence in the parameters on SLPs. Our methodology is based on the use of a popular technique, the bootstrap. We examine the applicability of the bootstrap for computing the confidence intervals for the estimated SLP parameters. In order to evaluate our methodology we concentrated on computation of confidence in the estimation of enzymatic reaction rates in amino acid pathway of Saccharomyces cerevisiae. Our results show that our bootstrap based methodology is useful in assessing the characteristics of the model and enables one to draw important statistical and biological inferences.