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Mining frequent patterns for AMP-activated protein kinase regulation on skeletal muscle.

Chen Q, Chen YP - BMC Bioinformatics (2006)

Bottom Line: AMP-activated protein kinase (AMPK) has emerged as a significant signaling intermediary that regulates metabolisms in response to energy demand and supply.In particular, the effect of AMPK on muscle cellular energy status makes this protein a promising pharmacological target for disease treatment.It is found that most of the extracted association rules have biological meaning and some of them were previously unknown.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Engineering & Information Technology, Deakin University, Melbourne, Australia. qifengch@deakin.edu.au

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 alpha, beta and gamma 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.

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The frequent patterns for AMPK regulation data.
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Figure 3: The frequent patterns for AMPK regulation data.

Mentions: The top-N interesting itemsets are then used to identify frequent patterns based on the defined criteria in subsection 'Discovering Association Rules', by which some uninteresting rules are pruned. We vary the minimum confidence starting from 0.4 to 1 by increasing 0.1 each time. Figure 3 shows the result of frequent patterns. We observe that there is no sharp drop in rule output when setting the minimum confidence from 0.7 to 1 in comparison with 0.6. Therefore, we select the results by 0.7 in contrast to the results by 0.6 in the following analysis. There are 74 and 51 association rules by minimum confidence 0.6 and 0.7, respectively. From these rules, we can find many potential correlations between itemsets. The rules by 0.7 are classified into the form of Rule1 (40 rules) and the form of Rule2 (11 rules) in terms of the definition in subsection 'Discovering Association Rules'. Nevertheless, the rules need to be pruned because some of them can overlap with each other. Suppose Ri: Ai → Bi and Rj: Aj → Bj are two rules. The pruning complies with the principles below:


Mining frequent patterns for AMP-activated protein kinase regulation on skeletal muscle.

Chen Q, Chen YP - BMC Bioinformatics (2006)

The frequent patterns for AMPK regulation data.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC1574354&req=5

Figure 3: The frequent patterns for AMPK regulation data.
Mentions: The top-N interesting itemsets are then used to identify frequent patterns based on the defined criteria in subsection 'Discovering Association Rules', by which some uninteresting rules are pruned. We vary the minimum confidence starting from 0.4 to 1 by increasing 0.1 each time. Figure 3 shows the result of frequent patterns. We observe that there is no sharp drop in rule output when setting the minimum confidence from 0.7 to 1 in comparison with 0.6. Therefore, we select the results by 0.7 in contrast to the results by 0.6 in the following analysis. There are 74 and 51 association rules by minimum confidence 0.6 and 0.7, respectively. From these rules, we can find many potential correlations between itemsets. The rules by 0.7 are classified into the form of Rule1 (40 rules) and the form of Rule2 (11 rules) in terms of the definition in subsection 'Discovering Association Rules'. Nevertheless, the rules need to be pruned because some of them can overlap with each other. Suppose Ri: Ai → Bi and Rj: Aj → Bj are two rules. The pruning complies with the principles below:

Bottom Line: AMP-activated protein kinase (AMPK) has emerged as a significant signaling intermediary that regulates metabolisms in response to energy demand and supply.In particular, the effect of AMPK on muscle cellular energy status makes this protein a promising pharmacological target for disease treatment.It is found that most of the extracted association rules have biological meaning and some of them were previously unknown.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Engineering & Information Technology, Deakin University, Melbourne, Australia. qifengch@deakin.edu.au

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 alpha, beta and gamma 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.

Show MeSH