* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download Interdisciplinary Data Science Faculty Candidate
Survey
Document related concepts
Immunoprecipitation wikipedia , lookup
List of types of proteins wikipedia , lookup
Circular dichroism wikipedia , lookup
Intrinsically disordered proteins wikipedia , lookup
Structural alignment wikipedia , lookup
Protein domain wikipedia , lookup
Protein moonlighting wikipedia , lookup
Bimolecular fluorescence complementation wikipedia , lookup
Protein design wikipedia , lookup
Rosetta@home wikipedia , lookup
Protein mass spectrometry wikipedia , lookup
Protein folding wikipedia , lookup
Western blot wikipedia , lookup
Protein purification wikipedia , lookup
Homology modeling wikipedia , lookup
Nuclear magnetic resonance spectroscopy of proteins wikipedia , lookup
Transcript
Interdisciplinary Data Science Faculty Candidate Jinbo Xu Toyota Technological Institute at Chicago Thursday, April 10, 2014 12:30 PM Goergen Hall, Room 101 Host: Prof. Jack Werren <[email protected]> Computational Methods for Data-Driven Study of Protein Structure and Function 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.