Mining frequent itemsets for protein kinase regulation

Publication Type:
Conference Proceeding
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2006, 4099 LNAI pp. 222 - 230
Issue Date:
Filename Description Size
Thumbnail2006005159.pdf675.02 kB
Adobe PDF
Full metadata record
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. © Springer-Verlag Berlin Heidelberg 2006.
Please use this identifier to cite or link to this item: