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Learning Dependency Model for AMP-Activated Protein Kinase Regulation.

Authors :
Carbonell, Jaime G.
Siekmann, Jörg
Zili Zhang
Yi-Ping Phoebe Chen
Qiumei Qin
Qingfeng Chen
Source :
Knowledge Science, Engineering & Management (978-3-540-76718-3); 2007, p221-229, 9p
Publication Year :
2007

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISBNs :
9783540767183
Database :
Complementary Index
Journal :
Knowledge Science, Engineering & Management (978-3-540-76718-3)
Publication Type :
Book
Accession number :
34019195
Full Text :
https://doi.org/10.1007/978-3-540-76719-0_24