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Non-parametric Bayesian Chromatin State Segmentation
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 high-throughput sequencing—is a common problem in genomics research. Recent large-scale 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 non-parametric Bayesian “sticky” HDP -HMM model for time-series segmentation, and introduce a variational mean-field 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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