|COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring.|
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.
This talk is included in these lists:
Note that ex-directory lists are not shown.
Other listsCambridge Journal of Regions, Economy and Society ETECH Projects Cambridge Society for the Application of Research (CSAR)
Other talksTalk by Managing Director of Abellio Greater Anglia Bioengineering Seminar Travelling to town: medieval peasants in the urban marketplace Governing Events: Emergencies and the Fragile Promise of the State The Craft of Spinning Classification of free Araki-Woods factors