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Learning Probabilistic Sequence Models for Uncovering Gene Regulation

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A central challenge in computational biology is to uncover the mechanisms and cellular circuits that govern how the expression of various genes is controlled in response to a cell’s environment. In this talk, I will discuss two aspects of my group’s work on learning probabilistic grammars to identify gene-regulatory elements in genomic sequences. First, I will talk about a method we have developed for modeling and predicting arbitrarily overlapping elements in sequence data. We have applied this algorithm to the task of analyzing bacterial genomes, in which it is common for functional elements in the genomes to overlap one another. Second, I will talk about an approach we have developed for learning expressive models of cis-regulatory elements (CRMs). A CRM is a configuration of sequence patterns that controls how a set of genes responds to specific conditions in a cell. This talk will not assume any prior knowledge of molecular biology, and for both algorithms, I will discuss why they are of interest for applications outside of biology.

This talk is part of the Computer Laboratory Wednesday Seminars series.

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