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2009

Inferring Gene Regulatory Networks from Asynchronous Microarray Data

10 years 18 days ago
Inferring Gene Regulatory Networks from Asynchronous Microarray Data
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. Asynchronous Inference of Regulatory Networks (AIRnet) provides gene signaling network inferrence using more practical assumptions about the microarray data. By learning correlation patterns from all pairs of microarray samples, accurate network reconstructions can be performed with data that is normally available in microarray experiments.
David Oviatt, Mark J. Clement, Quinn Snell, Kennet
Added 08 Nov 2010
Updated 08 Nov 2010
Type Conference
Year 2009
Where BIOCOMP
Authors David Oviatt, Mark J. Clement, Quinn Snell, Kenneth Sundberg, Jared Allen, Randall J. Roper
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