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.

[1]  Dario Floreano,et al.  Generating Realistic In Silico Gene Networks for Performance Assessment of Reverse Engineering Methods , 2009, J. Comput. Biol..

[2]  Gianluca Bontempi,et al.  minet: A R/Bioconductor Package for Inferring Large Transcriptional Networks Using Mutual Information , 2008, BMC Bioinformatics.

[3]  E. Feskens,et al.  Effects of interacting networks of cardiovascular risk genes on the risk of type 2 diabetes mellitus (the CODAM study) , 2008, BMC Medical Genetics.

[4]  E. Schuuring,et al.  Genome-wide promoter analysis uncovers portions of the cancer methylome. , 2008, Cancer research.

[5]  R. Sharan,et al.  Protein networks in disease. , 2008, Genome research.

[6]  Yoshihiro Yamanishi,et al.  KEGG for linking genomes to life and the environment , 2007, Nucleic Acids Res..

[7]  Qi Liu,et al.  Improving gene set analysis of microarray data by SAM-GS , 2007, BMC Bioinformatics.

[8]  Robert W. Li,et al.  Pathway analysis identifies perturbation of genetic networks induced by butyrate in a bovine kidney epithelial cell line , 2007, Functional & Integrative Genomics.

[9]  Chris J. Myers,et al.  Learning Genetic Regulatory Network Connectivity from Time Series Data , 2006, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[10]  A. Stepansky,et al.  Histidine biosynthesis in plants , 2006, Amino Acids.

[11]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[12]  Chiara Sabatti,et al.  Network component analysis: Reconstruction of regulatory signals in biological systems , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[13]  Fernando Carrari,et al.  Engineering central metabolism in crop species: learning the system. , 2003, Metabolic engineering.

[14]  L. Hood,et al.  A Genomic Regulatory Network for Development , 2002, Science.

[15]  F. Collins,et al.  Implications of the Human Genome Project for medical science. , 2001, JAMA.

[16]  T. Joshi,et al.  Inferring gene regulatory networks from multiple microarray datasets , 2006 .