title: A bayesian hierarchical model for the analysis of Affymetrix arrays.
abstract: An area of active research in DNA microarray analysis focuses on identifying differentially expressed genes between normal and malignant tissues. The analysis is complicated by the presence of several unreliable expression readings. Here, we illustrate a methodology where the expression estimates are modeled as censored data and discriminating genes are selected using ANOVA-based criteria.
authors.affiliation: Department of Statistics, Texas A&M University, TAMU 3143, College Station, TX 77843-3143, USA. mtadesse@stat.tamu.edu
authors.forename: Mahlet G
authors.initials: MG
authors.lastname: Tadesse
authors.forename: Joseph G
authors.initials: JG
authors.lastname: Ibrahim
mesh_terms: D000704:Analysis of Variance; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods; D015342:DNA Probes; D006801:Humans; D009369:Neoplasms / Q000175:diagnosis* / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*
chemical_list: D015342:DNA Probes