Parameter Estimation in Stochastic Logic Programs
Machine Learning
Biological applications of multi-relational data mining
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PRISM: a language for symbolic-statistical modeling
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Parameter learning of logic programs for symbolic-statistical modeling
Journal of Artificial Intelligence Research
Preprocessing for Optimization of Probabilistic-Logic Models for Sequence Analysis
ICLP '09 Proceedings of the 25th International Conference on Logic Programming
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Biological systems consist of many components and interactions between them. In Systems Biology the principal problem is modeling complex biological systems and reconstructing interactions between their building blocks. Symbolic machine learning approaches have the power to model structured domains and relations among objects. However biological domains require uncertainty handling due to their hidden complex nature. Statistical machine learning approaches have the potential to model uncertainty in a robust manner. In this paper we apply a hybrid symbolic-statistical framework to modeling metabolic pathways and show through experiments that complex phenomenon such as biochemical reactions in cell's metabolic networks can be modeled and simulated in the proposed framework.