This article is part of the supplement: The 2009 International Conference on Bioinformatics & Computational Biology (BioComp 2009)

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Inferring gene regulatory networks from asynchronous microarray data with AIRnet

David Oviatt1, Mark Clement1*, Quinn Snell1, Kenneth Sundberg1, Chun Wan J Lai2, Jared Allen3 and Randall Roper3

Author Affiliations

1 Department of Computer Science, Brigham Young University, Provo, UT, USA

2 Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT, USA

3 Department of Biology, Indiana University-Purdue University Indianapolis, Indianapolis, IN, USA

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BMC Genomics 2010, 11(Suppl 2):S6  doi:10.1186/1471-2164-11-S2-S6

Published: 2 November 2010



Modern approaches to treating genetic disorders, cancers and even epidemics rely on a detailed understanding of the underlying gene signaling network. Previous work has used time series microarray data to infer gene signaling networks given a large number of accurate time series samples. Microarray data available for many biological experiments is limited to a small number of arrays with little or no time series guarantees. When several samples are averaged to examine differences in mean value between a diseased and normal state, information from individual samples that could indicate a gene relationship can be lost.


Asynchronous Inference of Regulatory Networks (AIRnet) provides gene signaling network inference using more practical assumptions about the microarray data. By learning correlation patterns for the changes in microarray values from all pairs of samples, accurate network reconstructions can be performed with data that is normally available in microarray experiments.


By focussing on the changes between microarray samples, instead of absolute values, increased information can be gleaned from expression data.