Abstract
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.
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Lodhi, H., Muggleton, S. (2005). Modelling Metabolic Pathways Using Stochastic Logic Programs-Based Ensemble Methods. In: Danos, V., Schachter, V. (eds) Computational Methods in Systems Biology. CMSB 2004. Lecture Notes in Computer Science(), vol 3082. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25974-9_10
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DOI: https://doi.org/10.1007/978-3-540-25974-9_10
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