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Activity Number: 397 - Statistical Learning for Epigenomics Data
Type: Topic Contributed
Date/Time: Tuesday, July 31, 2018 : 2:00 PM to 3:50 PM
Sponsor: SSC
Abstract #330788 Presentation
Title: Understanding Gene Regulation Through Graph-Based Posterior Regularization in Structured Probabilistic Models
Author(s): Maxwell Libbrecht*
Companies: Simon Fraser University
Keywords: graphical models; time-series; regularization; optimization; biology; genomics

Graph-based based methods have been successful in solving many types of semi-supervised learning problems by optimizing a graph smoothness criterion. This criterion states that data instances nearby in a given graph are likely to have similar properties. A graph smoothness criterion cannot be directly incorporated into a generative unsupervised model because it is usually not clear what probabilistic process generated the data instances with respect to the graph, and incorporating the graph directly into a factorizable model (i.e. a time-series model such as an HMM) would break the model's factorizable structure, making exact inference methods (e.g. belief propagation) intractable. This method, called entropic graph-based posterior regularization (EGPR) provides a way to incorporate graph-based information into a probabilistic model by defining a regularization term on an auxiliary posterior distribution variable. We applied this approach to regulatory genomics data sets from the human genome, leading to the discovery of a new type of regulatory domain.

Authors who are presenting talks have a * after their name.

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