May 10th, 2013 @ 11:30am Narges Razavian (CMU)

Continuous Graphical Models for Protein Conformation Spaces

Generative models of protein structure enable researchers to predict the behavior of proteins under different conditions. These models, for instance, can help with the discovery of important pathways in proteins involved in virus infection process, and provide researchers with target regions that may be better candidates for drug development.

In this talk, we will focus on a series of graphical models developed to handle the challenges specific to the protein conformation modeling, which include ability to handle angular, multi-modal variables in very large datasets. We will first present von Mises graphical models which can handle angular variables. We then describe non-paranormal graphical models, also known as Gaussian copula, which can model arbitrary distributions and in theory, handle multi-modality. Finally we will present non-parametric kernel space embedded graphical models, which can handle multi-modal, arbitrary distributions via kernel density estimation. We will also briefly mention our solutions to make the kernel embedded models more scalable.

seminars/seminaritems/2013-05-10.txt · Last modified: 2013/05/05 19:07 by silberman