An analysis of first-order logics of probability
Artificial Intelligence
Machine Learning
Parameter Estimation in Stochastic Logic Programs
Machine Learning
Boosting the Margin Distribution
IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
A Unified Bias-Variance Decomposition for Zero-One and Squared Loss
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
ALT '96 Proceedings of the 7th International Workshop on Algorithmic Learning Theory
Computational methods for stochastic biological systems
Computational methods for stochastic biological systems
Boosting strategy for classification
Intelligent Data Analysis
A brief introduction to boosting
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Bootstrapping parameter estimation in dynamic systems
DS'11 Proceedings of the 14th international conference on Discovery science
Computing confidence measures in stochastic logic programs
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
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In this paper we present a methodology to estimate rates of enzymatic reactions in metabolic pathways. Our methodology is based on applying stochastic logic learning in ensemble learning. Stochastic logic programs provide an efficient representation for metabolic pathways and ensemble methods give state-of-the-art performance and are useful for drawing biological inferences. We construct ensembles by manipulating the data and driving randomness into a learning algorithm. We applied failure adjusted maximization as a base learning algorithm. The proposed ensemble methods are applied to estimate the rate of reactions in metabolic pathways of Saccharomyces cerevisiae. The results show that our methodology is very useful and it is effective to apply SLPs-based ensembles for complex tasks such as modelling of metabolic pathways.