title: Mixture modelling of gene expression data from microarray experiments.
abstract: BACKGROUND
  Hierarchical clustering is one of the major analytical tools for gene expression data from microarray experiments. A major problem in the interpretation of the output from these procedures is assessing the reliability of the clustering results. We address this issue by developing a mixture model-based approach for the analysis of microarray data. Within this framework, we present novel algorithms for clustering genes and samples. One of the byproducts of our method is a probabilistic measure for the number of true clusters in the data.
  
  
  RESULTS
  The proposed methods are illustrated by application to microarray datasets from two cancer studies; one in which malignant melanoma is profiled (Bittner et al., Nature, 406, 536-540, 2000), and the other in which prostate cancer is profiled (Dhanasekaran et al., 2001, submitted).
authors.affiliation: Department of Biostatistics, School of Public Health, University of Michigan, 1420 Washington Heights, Room M4057, Ann Arbor, MI 48109-2029, USA. ghoshd@umich.edu
authors.forename: Debashis
authors.initials: D
authors.lastname: Ghosh
authors.forename: Arul M
authors.initials: AM
authors.lastname: Chinnaiyan
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D006801:Humans; D008297:Male; D008545:Melanoma / Q000235:genetics; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*; D011471:Prostatic Neoplasms / Q000235:genetics; D012878:Skin Neoplasms / Q000235:genetics