Inferring gene regulatory networks from asynchronous microarray data with AIRnet

BMC Genomics

Research

Randall Roper¹  Jared Allen¹  Chun Wan J Lai²  Mark Clement³  David Oviatt³  Kenneth Sundberg³  Quinn Snell³ 

[1] Department of Biology, Indiana University-Purdue University Indianapolis, Indianapolis, IN, USA;Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT, USA;Department of Computer Science, Brigham Young University, Provo, UT, USA;

关键词: Microarray Data;    Regulatory Network;    Gene Regulatory Network;    Microarray Sample;    Network Component Analysis;   

DOI  :  10.1186/1471-2164-11-S2-S6

来源: Springer

【 摘 要 】

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

【 授权许可】

CC BY   
© Clement et al; licensee BioMed Central Ltd. 2010