May 23, 2002

Context-specific Bayesian clustering for gene expression data

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
Yoseph Barash, N Friedman

Abstract

The recent growth in genomic data and measurements of genome-wide expression patterns allows us to apply computational tools to examine gene regulation by transcription factors. In this work, we present a class of mathematical models that help in understanding the connections between transcription factors and functional classes of genes based on genetic and genomic data. Such a model represents the joint distribution of transcription factor binding sites and of expression levels of a gene in a unified probabilistic model. Learning a combined probability model of binding sites and expression patterns enables us to improve the clustering of the genes based on the discovery of putative binding sites and to detect which binding sites and experiments best characterize a cluster. To learn such models from data, we introduce a new search method that rapidly learns a model according to a Bayesian score. We evaluate our method on synthetic data as well as on real life data and analyze the biological insights it provides. Finally, we demonstrate the applicability of the method to other data analysis problems in gene expression data.

  • References15
  • Citations20

Citations

Mentioned in this Paper

Two-Parameter Models
Bayesian Prediction
Transcription Factor
MRNA Differential Display
Computational Molecular Biology
Disease Clustering
Early Promoters, Genetic

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