Classification of Tissue Samples Using Mixture Modeling of Microarray Gene Expression Data

Accurate classification of tissue samples is an essential tool in disease diagnosis and treatment. The DNA microarray technology enables disease classification based only on gene expression analysis, without prior biological insights. We present a classification method based on modeling the distribu...

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Bibliographic Details
Published in:Lecture notes-monograph series 2003-01, Vol.40, p.419-435
Main Authors: Lin, Shili, Roxana Alexandridis
Format: Article
Language:English
Subjects:
Datasets
Density estimation
Descriptive statistics
Gene expression
Information classification
Leukemia
Microarrays
Modeling
Parametric models
Simulation training
Tissue samples
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Summary:Accurate classification of tissue samples is an essential tool in disease diagnosis and treatment. The DNA microarray technology enables disease classification based only on gene expression analysis, without prior biological insights. We present a classification method based on modeling the distribution of the gene expression profile of a test sample as a mixture of distributions, each of which characterizes the levels of gene expression within a class. Class assignment for a test sample is based on the predictive probabilities of class memberships. We believe that this general modeling framework is a flexible scheme for multi-type classification. Since most of the thousands of genes whose expression levels are measured do not contribute to the separation between types of tissue samples, we also explore several measures for gene selection, including T, NPT, BW, NPBW, and a mixture modeling approach based on Markov chain Monte Carlo (MCMC) estimation of parameters. For a classifier based on a gene selection measure, such as the T classifier, the number of genes selected is achieved by cross-validation. The methods are applied to a leukemia dataset; our results are comparable with the best results achieved in a comparative study done by Professor Terry Speed and colleagues.
ISSN:0749-2170