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A Systematic Approach for Identifying Regulatory Interactions in Large Temporal Gene Expression Datasets from Peripheral Blood

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3 Author(s)
S. Knott ; Dept of Computer Science, Queen's University, Kingston, Ontario, Canada, K7L 3N6 email: ; P. Mousavi ; S. Baranzini

High throughput genomic techniques produce datasets involving thousands of gene expression profiles. In order to infer biologically meaningful regulatory interactions, a dimensionality reduction must take place to identify genes or groups of genes that are important to the biological system being analyzed. Here we provide a systematic approach to remove dispersible genes from consideration based on their gene expression profiles, and to identify a smaller set of coordinately expressed genes, or metagenes that are biologically related to one and other based on previous biological knowledge. We then apply neural network based reverse engineering techniques to demonstrate that through these dimensionality reduction techniques novel genetic interactions can be identified

Published in:

2006 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology

Date of Conference:

28-29 Sept. 2006