Williamson, J. and Bang, Jung-Wook and Chaleil, Raphael (2003) Two-stage Bayesian networks for metabolic network prediction. In: Model-based and Qualitative Reasoning in Biomedicine. pp. 19-23.
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Metabolism is a set of chemical reactions, used by living organisms to process chemical compounds in order to take energy and eliminate toxic compounds, for example. Its processes are referred as metabolic pathways. Understanding metabolism is imperative to biology, toxicology and medicine, but the number and complexity of metabolic pathways makes this a difficult task. In our paper, we investigate the use of causal Bayesian networks to model the pathways of yeast saccharomyces cerevisiae metabolism: such a network can be used to draw predictions about the levels of metabolites and enzymes in a particular specimen. We propose a two-stage methodology for causal networks, as follows. First construct a causal network from the network of metabolic pathways. The viability of this causal network depends on the validity of the causal Markov condition. If this condition fails, however, the principle of the common cause motivates the addition of a new causal arrow or a new `hidden' common cause to the network (stage 2 of the model formation process). Algorithms for adding arrows or hidden nodes have been developed separately in a number of papers, and in this paper we combine them, showing how the resulting procedure can be applied to the metabolic pathway problem. Our general approach was tested on neural cell morphology data and demonstrated noticeable improvements in both prediction and network accuracy.
|Item Type:||Conference or workshop item (Paper)|
|Subjects:||B Philosophy. Psychology. Religion > B Philosophy (General)
Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Q Science > QH Natural history > QH301 Biology
|Divisions:||Faculties > Humanities > School of European Culture and Languages|
|Depositing User:||Jon Williamson|
|Date Deposited:||07 Sep 2008 14:34|
|Last Modified:||20 Jan 2012 15:05|
|Resource URI:||http://kar.kent.ac.uk/id/eprint/7453 (The current URI for this page, for reference purposes)|
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