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Bioinformatics Advance Access originally published online on September 16, 2004
Bioinformatics 2005 21(4):509-516; doi:10.1093/bioinformatics/bti026
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Bioinformatics vol. 21 issue 4 © Oxford University Press 2005; all rights reserved.

Identifying time-lagged gene clusters using gene expression data

Liping Ji * and Kian-Lee Tan

Department of Computer Science, National University of Singapore 3 Science Drive 2, Singapore 117543, Singapore

*To whom correspondence should be addressed.

Motivation: Analysis of gene expression data can provide insights into the time-lagged co-regulation of genes/gene clusters. However, existing methods such as the Event Method and the Edge Detection Method are inefficient as they compare only two genes at a time. More importantly, they neglect some important information due to their scoring criterian. In this paper, we propose an efficient algorithm to identify time-lagged co-regulated gene clusters. The algorithm facilitates localized comparison and processes several genes simultaneously to generate detailed and complete time-lagged information for genes/gene clusters.

Results: We experimented with the time-series Yeast gene dataset and compared our algorithm with the Event Method. Our results show that our algorithm is not only efficient, but also delivers more reliable and detailed information on time-lagged co-regulation between genes/gene clusters.

Availability: The software is available upon request.

Contact: jiliping{at}comp.nus.edu.sg

Supplementary information: Supplementary tables and figures for this paper can be found at http://www.comp.nus.edu.sg/~jiliping/p2.htm.


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