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Reliable detection of short periodic gene expression time series profiles in DNA microarray data

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2 Author(s)
Alan Wee-Chung Liew ; School of Information and Communication Technology, Gold Coast Campus, Griffith University, QLD4222, Australia ; Hong Yan

Many cellular processes exhibit cyclic behaviors. Hence, one important task in gene expression data analysis is to detect subset of genes that exhibit periodicity in their gene expression time series profiles. Unfortunately, gene expression time series profiles are usually of very short length, with very few periods, unevenly sampled, and are highly contaminated with noise. This makes detection of periodic profiles a very challenging problem. In this paper, we present several effective computational techniques developed recently in our research group for the reliable detection of short periodic gene expression time series profiles.

Published in:

Systems, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on

Date of Conference:

11-14 Oct. 2009