title: Inferring regulatory networks.
abstract: The discovery of regulatory networks is an important aspect in the post genomic research. The process requires integrated efforts of experimental and computational strategies by employing the systems biology approach. This review summarizes some of the major themes in computational inference of regulatory networks based on gene expression and other data sources, including transcriptional module identification, network topology inference, and network analysis. Popular solutions to each of these problems and their relative merits are discussed.
authors.affiliation: Bioinformatics Unit, Branch of Research Resources, National Institute on Aging, NIH, Baltimore, MD, USA. huaili@mail.nih.gov <huaili@mail.nih.gov>
authors.forename: Huai
authors.initials: H
authors.lastname: Li
authors.forename: Jianhua
authors.initials: J
authors.lastname: Xuan
authors.forename: Yue
authors.initials: Y
authors.lastname: Wang
authors.forename: Ming
authors.initials: M
authors.lastname: Zhan
mesh_terms: D000465:Algorithms; D000818:Animals; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D006801:Humans; D008390:Markov Chains; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis; D012984:Software; D049490:Systems Biology; D014158:Transcription, Genetic