Members of the PSI Lab study machine learning, genome biology and vision. We develop statistical inference and probabilistic reasoning (graphical modeling) methods for learning about complex patterns in data. In our genome biology research, we are interested in understanding the regulatory processes that enable a limited number of genes to generate a much more massive and diverse set of genetic messages. In our vision research, we focus on developing probabilistic generative models for scene and object analysis. The group is led by Brendan J. Frey in the Department of Electrical and Computer Engineering, with cross appointments in Computer Science, Banting and Best Department of Medical Research and Donnelly Centre for Cellular and Biomolecular Research. If you are interested in joining the group, click here.

Latest News
11/2015: Two of the Top 10 papers in 2015 came from our lab, as selected by RECOMB Regulatory and Systems Genomics.
07/2015: Frey lab launches a startup company Deep Genomics to transform precision medicine, genetic testing, diagnostics and the development of therapies.

Current Research Highlight

Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning

Regulatory motif predictions from DeepBind

The August issue of Nature Biotechnology highlighted our latest work on the cover. We show that deep learning architecture can discover the binding specificities of DNA and RNA-binding proteins from raw sequences. By accurately modelling how proteins bind to DNA and RNA, we can predict how mutations affect binding --- a key building block for computational identification of disease-causing mutations.


Reference

Babak Alipanahi, Andrew Delong, Matthew T Weirauch and Brendan J Frey. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nature Biotechnology doi:10.1038/nbt.3300. Published online July 27 2015. [DeepBind article] [DeepBind web page]