title: Prediction and uncertainty in the analysis of gene expression profiles.
abstract: We have developed a complete statistical model for the analysis of tumor specific gene expression profiles. The approach provides investigators with a global overview on large scale gene expression data, indicating aspects of the data that relate to tumor phenotype, but also summarizing the uncertainties inherent in classification of tumor types. We demonstrate the use of this method in the context of a gene expression profiling study of 27 human breast cancers. The study is aimed at defining molecular characteristics of tumors that reflect estrogen receptor tatus. In addition to good predictive performance with respect to pure classification of the expression profiles, the model also uncovers conflicts in the data with respect to the classification of some of the tumors, highlighting them as critical cases for which additional investigations are appropriate.
authors.affiliation: Institute of Statistics and Decision Sciences, Duke University, Durham, NC, USA.
authors.forename: Rainer
authors.initials: R
authors.lastname: Spang
authors.forename: Harry
authors.initials: H
authors.lastname: Zuzan
authors.forename: Mike
authors.initials: M
authors.lastname: West
authors.forename: Joseph
authors.initials: J
authors.lastname: Nevins
authors.forename: Carrie
authors.initials: C
authors.lastname: Blanchette
authors.forename: Jeffrey R
authors.initials: JR
authors.lastname: Marks
mesh_terms: D001499:Bayes Theorem; D020869:Gene Expression Profiling; D008962:Models, Theoretical