[Colloquium] Seminar Announcement: Statistical Learning Machines for Protein Structure Prediction in the Era of High-Throughput Sequencing

[Ninfa Mayorga]

~REMINDER

Computation Institute Presentation

Speaker: Jinbo Xu, Toyota Technological Institute at Chicago
Host:  Ian Foster
Date: October 17, 2013
Time: 1:00 PM - 2:00 PM
Location:  University of Chicago, Searle 240A, 5735 S. Ellis Ave.  This talk will be broadcast via Adobe Connect (see below)

Statistical Learning Machines for Protein Structure Prediction in the Era of High-Throughput Sequencing

Abstract:  If we know the primary sequence of a protein, can we predict its 3D structure by computational methods? This is one of the most important and challenging problems in computational molecular biology and has tremendous implications for the understanding of life process, diseases and drug discovery. Depending on whether or not there is one solved structure similar to the protein sequence under consideration, computational methods for protein folding can be classified into two categories: template-based and template-free modeling. The former uses similar solved structures as templates to predict the structure of a protein while the latter does not. This talk will demonstrate how statistical learning methods especially probabilistic graphical models can be applied to address some fundamental challenges facing template-based and template-free protein folding by taking advantage of high-throughput sequencing and protein structure initiatives.

Information:  Lunch will be provided

This talk will be broadcast to Argonne National Laboratory, TCS Building 240, Room 5172. 

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