Abstract
The AMP-activated protein kinase (AMPK) acts as a metabolic master switch regulating several intracellular systems. The effect of AMPK on muscle cellular energy status makes this protein a promising pharmacological target for disease treatment. With increasingly available AMPK regulation data, it is critical to develop an efficient way to analyze the data since this assists in further understanding AMPK pathways. Bayesian networks can play an important role in expressing the dependency and causality in the data. This paper aims to analyse the regulation data using B-Course, a powerful analysis tool to exploit several theoretically elaborate results in the fields of Bayesian and causal modelling, and discover a certain type of multivariate probabilistic dependencies. The identified dependency models are easier to understand in comparison with the traditional frequent patterns.
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© 2007 Springer-Verlag Berlin Heidelberg
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Chen, YP.P., Qin, Q., Chen, Q. (2007). Learning Dependency Model for AMP-Activated Protein Kinase Regulation. In: Zhang, Z., Siekmann, J. (eds) Knowledge Science, Engineering and Management. KSEM 2007. Lecture Notes in Computer Science(), vol 4798. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76719-0_24
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DOI: https://doi.org/10.1007/978-3-540-76719-0_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76718-3
Online ISBN: 978-3-540-76719-0
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