Finding Regulatory Elements Using Joint Likelihoods for Sequence and Expression Profile Data

Proceedings of the Twentieth International Conference on Machine Learning, 2000

Volume

Proceedings of the Twentieth International Conference on Machine Learning, 2000

Contents

A recent popular method of finding promoter sequences is to look for conserved motifs upstream of genes clustered on the basis of expression data. This method presupposes that the clustering is correct. Theoreticall, one should be better able to find promoter sequences and create more relevant gene clusters by taking a unified approach to these two problems. We present a likelihood function for a sequence-expression model giving a joint likelihood for a promoter sequence and its corresponding expression levels. An algorithm to estimate sequence-expression model parameters using Gibbs sampling and Expectation/Maximization is described. A program called kimono that implements this algorithm has been developed.