By Topic

Gene trajectory clustering with a hybrid genetic algorithm and expectation maximization method

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Z. S. H. Chan ; Knowledge Eng. & Discovery Res. Inst., Auckland Univ. of Technol., New Zealand ; N. Kasabov

Clustering time course gene expression data (gene trajectories) is an important step towards solving the complex problem of gene regulatory network (GRN) modeling and discovery as it significantly reduces the dimensionality of the gene space required for analysis. This paper introduces a novel method that hybridizes genetic algorithm (GA) and expectation maximization algorithms (EM) for clustering with the mixtures of multiple linear regression models (MLRs). The proposed method is applied to cluster gene expression time course data into smaller number of classes based on their trajectory similarities. Its performance and application as a generic clustering method to other complex problems are discussed.

Published in:

Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on  (Volume:3 )

Date of Conference:

25-29 July 2004