Nonparametric Bayesian Chromatin State Segmentation
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If you have a question about this talk, please contact Zoubin Ghahramani.
Chromatin state segmentation—the division of a genome into regions of similar combinatorial patterns of DNA or histone modifications, as measured through highthroughput sequencing—is a common problem in genomics research. Recent largescale projects have generated enormous amounts of chromatin state information,
without corresponding advances in techniques for analyzing with these large, complex datasets.
In this talk, I will first outline the problem of chromatin state segmentation and discuss current approaches. I will then review the nonparametric Bayesian “sticky” HDP HMM model for timeseries segmentation, and introduce a variational meanfield algorithm for inference in the sticky HDP HMM with an application to chromatin state segmentation.
This talk is part of the Machine Learning @ CUED series.
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