Gene specific co-regulation discovery: an improved approach

Zhang, Ji and Liu, Qing and Xu, Kai (2009) Gene specific co-regulation discovery: an improved approach. In: 2009 International Conference on Computational Science (ICCS 2009): Compute. Discover. Innovate., 25-27 May 2009, Baton Rouge, Louisiana, United States.

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Abstract

[Abstract]: Discovering gene co-regulatory relationships is a new but important research problem in DNA microarray data analysis. The problem of gene specific co-regulation discovery is to, for a particular gene of interest, called the target gene, identify its strongly co-regulated genes and the condition subsets where such strong gene co-regulations are observed. The study on this problem can contribute to a better understanding and characterization of the target gene. The existing method, using the genetic algorithm (GA), is slow due to its expensive fitness evaluation and long individual representation. In this paper, we propose an improved method for finding gene specific co-regulations. Compared with the current method, our method features a notably improved effciency. We employ kNN Search Table to substantially speed up fitness evaluation in the GA. We also propose a more compact representation scheme for encoding individuals in the GA, which contributes to faster crossover and mutation operations. Experimental results with a real-life gene mi-croarray data set demonstrate the improved effciency of our technique compared with the current method.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Author's version deposited in accordance with the copyright policy of the publisher. The original publication is available at www.springerlink.com)
Depositing User: Dr Ji Zhang
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 23 Sep 2009 02:31
Last Modified: 02 Jul 2013 23:23
Uncontrolled Keywords: genes; gene co-regulatory relationships; gene specific co-regulation
Fields of Research (FOR2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
URI: http://eprints.usq.edu.au/id/eprint/5621

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