title: Reconstructing transcriptional regulatory networks through genomics data.
abstract: One central problem in biology is to understand how gene expression is regulated under different conditions. Microarray gene expression data and other high throughput data have made it possible to dissect transcriptional regulatory networks at the genomics level. Owing to the very large number of genes that need to be studied, the relatively small number of data sets available, the noise in the data and the different natures of the distinct data types, network inference presents great challenges. In this article, we review statistical and computational methods that have been developed in the last decade in response to genomics data for inferring transcriptional regulatory networks.
authors.affiliation: Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT 06520, USA. ning.sun@yale.edu
authors.forename: Ning
authors.initials: N
authors.lastname: Sun
authors.forename: Hongyu
authors.initials: H
authors.lastname: Zhao
mesh_terms: D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D023281:Genomics; D006801:Humans; D016011:Normal Distribution