Please use this identifier to cite or link to this item: https://doi.org/10.1186/1471-2105-7-394
Title: Mining frequent patterns for AMP-activated protein kinase regulation on skeletal muscle
Authors: Chen, Q 
Chen, Y.-P.P
Keywords: AMP-activated protein kinase
Co-relationships
Disease treatment
Itemset generation
Market basket analysis
Metabolic action
Metabolic pathways
Physical training
Energy management
Metabolism
Muscle
Data mining
hydroxymethylglutaryl coenzyme A reductase kinase
isoenzyme
hydroxymethylglutaryl coenzyme A reductase kinase
isoenzyme
multienzyme complex
protein serine threonine kinase
protein subunit
alpha chain
article
beta chain
controlled study
correlation analysis
energy consumption
enzyme activity
exercise
human
information processing
muscle training
prediction
protein expression
protein function
protein metabolism
skeletal muscle
algorithm
computer program
diabetes mellitus
energy metabolism
gene expression regulation
genetics
mathematical computing
metabolism
protein subunit
signal transduction
skeletal muscle
Algorithms
AMP-Activated Protein Kinases
Diabetes Mellitus
Energy Metabolism
Exercise
Gene Expression Regulation, Enzymologic
Humans
Isoenzymes
Mathematical Computing
Multienzyme Complexes
Muscle, Skeletal
Protein Subunits
Protein-Serine-Threonine Kinases
Signal Transduction
Software
Issue Date: 2006
Citation: Chen, Q, Chen, Y.-P.P (2006). Mining frequent patterns for AMP-activated protein kinase regulation on skeletal muscle. BMC Bioinformatics 7 : 394. ScholarBank@NUS Repository. https://doi.org/10.1186/1471-2105-7-394
Rights: Attribution 4.0 International
Abstract: Background: AMP-activated protein kinase (AMPK) has emerged as a significant signaling intermediary that regulates metabolisms in response to energy demand and supply. An investigation into the degree of activation and deactivation of AMPK subunits under exercise can provide valuable data for understanding AMPK. In particular, the effect of AMPK on muscle cellular energy status makes this protein a promising pharmacological target for disease treatment. As more AMPK regulation data are accumulated, data mining techniques can play an important role in identifying frequent patterns in the data. Association rule mining, which is commonly used in market basket analysis, can be applied to AMPK regulation. Results: This paper proposes a framework that can identify the potential correlation, either between the state of isoforms of ?, ? and ? subunits of AMPK, or between stimulus factors and the state of isoforms. Our approach is to apply item constraints in the closed interpretation to the itemset generation so that a threshold is specified in terms of the amount of results, rather than a fixed threshold value for all itemsets of all sizes. The derived rules from experiments are roughly analyzed. It is found that most of the extracted association rules have biological meaning and some of them were previously unknown. They indicate direction for further research. Conclusion: Our findings indicate that AMPK has a great impact on most metabolic actions that are related to energy demand and supply. Those actions are adjusted via its subunit isoforms under specific physical training. Thus, there are strong co-relationships between AMPK subunit isoforms and exercises. Furthermore, the subunit isoforms are correlated with each other in some cases. The methods developed here could be used when predicting these essential relationships and enable an understanding of the functions and metabolic pathways regarding AMPK. © 2006 Chen and Chen; licensee BioMed Central Ltd.
Source Title: BMC Bioinformatics
URI: https://scholarbank.nus.edu.sg/handle/10635/178015
ISSN: 14712105
DOI: 10.1186/1471-2105-7-394
Rights: Attribution 4.0 International
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