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Machine Learning Methods for Uncovering cis-Regulatory Modules

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The process of transcription is often regulated by systems of factors which bind in specific arrangements, called cis-regulatory modules (CRMs), in promoter regions. I will describe a discriminative, machine-learning approach we have developed for uncovering CRMs in the promoter regions of genes that seem to be co-regulated. Our approach simultaneously learns models of binding-site motifs as well as the logical structure and spatial preferences of these motifs in CRMs. Our results on yeast data sets show better predictive accuracy than a current state-of-the-art approach on the same data sets. Our results on yeast, fly, and human data sets show that the inclusion of logical and spatial aspects improves the predictive accuracy of our learned models.

This talk is part of the Computational and Systems Biology series.

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