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.