title: From microarrays to networks: mining expression time series.
abstract: Over the past few years, powerful new methods have been devised that enable researchers to study the expression dynamics of many genes simultaneously (e.g. gene expression profiles using cDNA microarrays). In principle, this potentially vast quantity of data enables the dissection of the complex genetic networks that control the patterns and rhythms of gene expression in the cell. Finding the patterns in those data represents the next major phase in our understanding of the programming and functioning of the living cell. Simple dynamic models can be used to generate gene expression networks. These networks reveal the phenomenological link between the expression of different genes. This review discuss how these networks are generated and outlines several data-mining techniques for extracting relationships and hypotheses in gene expression. These emerging methods can be applied to a range of biological problems.
authors.affiliation: Keck Graduate Institute of Applied Life Sciences, 535 Watson Drive, Claremont, CA 91711, USA. greg_dewey@kgi.edu
authors.forename: T Gregory
authors.initials: TG
authors.lastname: Dewey
mesh_terms: D019295:Computational Biology / Q000639:trends*; D030561:Databases, Nucleic Acid; D015870:Gene Expression / Q000235:genetics*; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis; D010600:Pharmacology / Q000639:trends; D013997:Time Factors