Learning dependency model for AMP-activated protein kinase regulation

Chen, Yi-Ping Phoebe, Qin, Q. and Chen, Qingfeng 2007, Learning dependency model for AMP-activated protein kinase regulation, Lecture notes in computer science, vol. 4798, pp. 221-229, doi: 10.1007/978-3-540-76719-0_24.

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Title Learning dependency model for AMP-activated protein kinase regulation
Author(s) Chen, Yi-Ping Phoebe
Qin, Q.
Chen, Qingfeng
Journal name Lecture notes in computer science
Volume number 4798
Start page 221
End page 229
Publisher Springer Berlin
Place of publication Berlin, Germany
Publication date 2007
ISSN 0302-9743
Summary 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.
Language eng
DOI 10.1007/978-3-540-76719-0_24
Field of Research 080199 Artificial Intelligence and Image Processing not elsewhere classified
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2007, Springer-Verlag Berlin Heidelberg
Persistent URL http://hdl.handle.net/10536/DRO/DU:30007575

Document type: Journal Article
Collection: School of Engineering and Information Technology
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