Context-specific Bayesian clustering for gene expression data

Y. Barash*, N. Friedman

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

35 Scopus citations

Abstract

The recent growth in genomic data and measurement of genome-wide expression patterns allows to examine gene regulation by transcription factors using computational tools. 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. These models represent the joint distribution of transcription factor binding sites and of expression levels of a gene in a single 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 data and analyze the biological insights it provides.

Original languageAmerican English
Pages12-21
Number of pages10
DOIs
StatePublished - 2001
Event5th Annual Internatinal Conference on Computational Biology - Montreal, Que., Canada
Duration: 22 May 200126 May 2001

Conference

Conference5th Annual Internatinal Conference on Computational Biology
Country/TerritoryCanada
CityMontreal, Que.
Period22/05/0126/05/01

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