Computational Methods for Data-Driven Study of Protein Structure and Function
April 10, 2014
12:30 PM - 01:30 PM
Goergen Hall, Room 101
High-throughput sequencing has been producing a large amount of protein sequences, but many of them are missing solved structures and functional annotations, which are essential to the understanding of life process and diseases and also have tremendous implications to drug discovery and design. This talk will focus on protein homology detection and knowledge-based structure prediction, which are widely used for the elucidation of protein structure and function as well as protein evolutionary relationship. In particular, this talk will demonstrate how statistical machine learning (e.g., probabilistic graphical models) and optimization methods can be applied to address some fundamental challenges facing protein homology detection and protein folding by taking advantage of high-throughput sequencing.
Dr. Jinbo Xu is an associate professor at the Toyota Technological Institute at Chicago, a computer science research and educational institute located at the University of Chicago, and a research affiliate of the MIT Computer Science and Artificial Intelligence Laboratory. Dr. Xu’s research lies in machine learning, optimization and computational biology (especially protein bioinformatics and biological network analysis). He has developed several popular bioinformatics programs such as the CASP-winning RaptorX (http://raptorx.uchicago.edu) for protein structure prediction and IsoRank for comparative analysis of protein interaction networks. Dr. Xu is the recipient of Alfred P. Sloan Research Fellowship and NSF CAREER award.