Abstract:
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.
