Learning dependency model for AMP-activated protein kinase regulation

  • Authors:

  • Yi-Ping Phoebe Chen;Qiumei Qin;Qingfeng Chen

  • Affiliations:

  • School of Engineering & Information Technology, Deakin University, Australia and Australia Research Council Centre in Bioinformatics, Australia;School of Engineering & Information Technology, Deakin University, Australia and Zhuhai State-owned Assets Supervision and Management Commission, China;School of Engineering & Information Technology, Deakin University, Australia

  • Venue:
  • KSEM'07 Proceedings of the 2nd international conference on Knowledge science, engineering and management
  • Year:
  • 2007

Quantified Score

Hi-index 0.00

Visualization

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.