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Machine Learning Methods for Data-Driven Study of Protein Structure and Functio

Speaker: Prof. Jinbo Xu Toyota Technological Institute at Chicago
Time: 2015-11-23 16:00-2015-11-23 17:00
Venue: FIT 1-222


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 data-driven computational methods for the elucidation of protein structure and function and evolutionary relationship. In particular,this talk will present the modeling of multiple protein sequence alignment (MSA) by probabilistic graphical models (e.g., joint graphical lasso) and its application to a few challenging problems including protein remote homology detection, evolutionary coupling analysis and protein contact prediction. The technical details are described in the following two papers:http://bioinformatics.oxfordjournals.org/content/early/2015/08/14/bioinformatics.btv472 and http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003500  .

Short Bio:

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, a Senior Fellow at the Computational Institute of 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.