Abstract
This paper describes research in Probabilistic Inductive Logic Programing (PILP). The question investigated is whether PILP should always be used to learn from categorical examples. The data sets used by most PILP systems and applications have non-probabilistic class values, like those used in ILP systems. The main reason for this is the lack of an obvious source of probabilistic class values. In this context, we investigate the use of Abductive Stochastic Logic Programs (SLPs) for metabolic network learning.
One of the machine learning approaches, which has been used to model the inhibitory effect of various toxins in the metabolic network of rats, is abductive ILP [3]. A group of rats are injected with a toxin and the changes on the concentrations of a number of chemical compounds are monitored over time. The binary information on up/down regulations of metabolite concentrations is combined with background knowledge representing a subset of the KEGG metabolic diagrams. An abductive ILP program is used to suggest the inhibitory effects occurring in the network.c
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References
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Chen, J., Muggleton, S., Santos, J. (2008). Learning Probabilistic Logic Models from Probabilistic Examples (Extended Abstract). In: Blockeel, H., Ramon, J., Shavlik, J., Tadepalli, P. (eds) Inductive Logic Programming. ILP 2007. Lecture Notes in Computer Science(), vol 4894. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78469-2_3
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DOI: https://doi.org/10.1007/978-3-540-78469-2_3
Publisher Name: Springer, Berlin, Heidelberg
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