Mining frequent itemsets for protein kinase regulation

Chen, Qingfeng, Chen, Yi-Ping Phoebe, Zhang, Chengqi and Li, Lianggang 2006, Mining frequent itemsets for protein kinase regulation, Lecture notes in artificial intelligence, vol. 4099, pp. 222-230.

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Title Mining frequent itemsets for protein kinase regulation
Author(s) Chen, Qingfeng
Chen, Yi-Ping Phoebe
Zhang, Chengqi
Li, Lianggang
Journal name Lecture notes in artificial intelligence
Volume number 4099
Start page 222
End page 230
Publisher Springer-Verlag
Place of publication Heidelberg, Germany
Publication date 2006
ISSN 0302-9743
Summary Protein kinases, a family of enzymes, have been viewed as an important signaling intermediary by living organisms for regulating critical biological processes such as memory, hormone response and cell growth. The
unbalanced kinases are known to cause cancer and other diseases. With the increasing efforts to collect, store and disseminate information about the entire kinase family, it not only leads to valuable data set to understand cell regulation but also poses a big challenge to extract valuable knowledge about metabolic pathway from the data. Data mining techniques that have been widely used to find frequent patterns in large datasets can be extended and adapted to kinase data as well. This paper proposes a framework for mining frequent itemsets from the collected kinase dataset. An experiment using AMPK regulation data demonstrates that our approaches are useful and efficient in analyzing kinase regulation data.
Language eng
Field of Research 080610 Information Systems Organisation
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2006, Springer-Verlag
Persistent URL http://hdl.handle.net/10536/DRO/DU:30003616

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