title: Correlated Discretized Expression score: a method for identifying gene interaction networks from time course microarray expression data.
abstract: One of the goals of genomic expression analysis is to construct gene interaction networks from microarray data. Time course microarray data is a common place to seek causal relationships between the expression of a regulator and its effect on the expression of its targets. By proposing gene expression patterns of regulator and target genes based on biological expectation of regulatory interactions, it is possible to propose a system to identify these patterns. This system is based on the Correlated Discretized Expression (CDE) score calculated from microarray time course data. The CDE-score is derived by discretizing microarray data to identify significant gene expression changes. The usefulness of this method is demonstrated using a set of hypothetical gene expression data and the analysis of S. cerevisiae cell cycle microarray data.
authors.affiliation: Core Genomics Lab., Illinois Univ., Chicago, IL 60607-7058, USA. plarsen@uic.edu
authors.forename: Peter
authors.initials: P
authors.lastname: Larsen
authors.forename: Eyad
authors.initials: E
authors.lastname: Almasri
authors.forename: Guanrao
authors.initials: G
authors.lastname: Chen
authors.forename: Yang
authors.initials: Y
authors.lastname: Dai
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D001330:Electronic Data Processing; D015870:Gene Expression; D020869:Gene Expression Profiling; D015966:Gene Expression Regulation, Fungal; D053263:Gene Regulatory Networks; D008957:Models, Genetic; D015233:Models, Statistical; D008962:Models, Theoretical; D020411:Oligonucleotide Array Sequence Analysis; D012441:Saccharomyces cerevisiae / Q000235:genetics*; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics*; D013997:Time Factors
chemical_list: D029701:Saccharomyces cerevisiae Proteins