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Discovering Temporal Associations among Significant Changes in Gene Expression
 

Summary: Discovering Temporal Associations among Significant Changes
in Gene Expression
Hashmat Rohian, Aijun An, Jiashu Zhao, Jimmy Huang
Department of Computer Science and Engineering, York University
Toronto, Canada
{rohian, aan, jessie, jhuang}@cse.yorku.ca
Abstract-- One of the most demanding problems in mining
temporal data is to identify how multivariate change
associations might be discovered and used to better
understand data interactions and dependencies. This paper
introduces a framework to mine associations among
significant changes in multivariate time-series data. Building
on statistical methods, we detect significant changes in time-
series data and use marginal change rates to qualify the
direction of change at significant change points.
Furthermore, a propositional confirmation-guided rule
discovery method is used to discover associations among
these significant changes. We apply our approach to gene
expression data measured in yeast cell cycles and
demonstrate that our method can learn novel and high-

  

Source: An, Aijun - Department of Computer Science, York University (Toronto)

 

Collections: Computer Technologies and Information Sciences