UTMIST Talk Series | ML for Genomic Data Analysis: Segway and the Graphical Models Toolkit


UTMIST was thrilled to have Professor Michael Hoffman with us to talk about his work in computational biology and machine learning. Watch the event recording to learn more.

Professor Hoffman presented Segway, a widely-used method for analyzing multiple tracks of functional genomics data. Segway is implemented using the Graphical Models Toolkit, a flexible system for efficient dynamic Bayesian network (DBN) inference. This method is the first application of DBN techniques to genome-scale data and the first genomic segmentation method designed for use with the maximum resolution data available from ChIP-seq experiments without downsampling. Segway is a publicly available software package that will allow us to better study disease epigenomics.

Note: No prerequisite is required for the talk.

Event Details


Presentation on Segway and the Graphical Models Toolkit (5:00 - 5:50PM) Q&A with Prof. Michael Hoffman (5:50 - 6:00PM)

About the Presenter

Professor Michael Hoffman is a principal investigator at Princess Margaret Cancer Centre and Associate Professor in the Departments of Medical Biophysics and Computer Science, University of Toronto. He creates predictive computational models to understand interactions between genome, epigenome, and phenotype in human cancers. If you would like to know more about his work, you can check out his lab website here.