Machine Learning
Parameter Estimation in Stochastic Logic Programs
Machine Learning
Boosting strategy for classification
Intelligent Data Analysis
A comparison of some error estimates for neural network models
Neural Computation
A brief introduction to boosting
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Modelling metabolic pathways using stochastic logic programs-based ensemble methods
CMSB'04 Proceedings of the 20 international conference on Computational Methods in Systems Biology
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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.