Inferring gene regulatory networks from asynchronous microarray data with AIRnet.
BACKGROUND
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.


RESULTS
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.


CONCLUSIONS
By focussing on the changes between microarray samples, instead of absolute values, increased information can be gleaned from expression data.
Department of Computer Science, Brigham Young University, Provo, UT, USA. xkordave@gmail.com
David
D
Oviatt
Mark
M
Clement
Quinn
Q
Snell
Kenneth
K
Sundberg
Chun Wan J
CW
Lai
Jared
J
Allen
Randall
R
Roper
D000465:Algorithms; D000818:Animals; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D051379:Mice; D020411:Oligonucleotide Array Sequence Analysis