报告摘要：In this talk, I will present my understanding on the analogy between protein sequence and natural language. Inspired by such analogy, many machine learning techniques used in NLP can be applied to proteins. In particular, probabilistic graphical models provide a natural representation for both protein sequence and structure. I will introduce several graphical models for protein sequence modeling, structure prediction and function prediction. I will also discuss their analogies to the tasks in NLP. Finally, I will discuss some recent progress and potential future directions for graphical models.

报告人简介：Jian Peng is an Assistant Professor in Computer Science at UIUC. Before joining UIUC, he worked as a postdoctoral researcher at MIT CSAIL. He received his PhD from TTI-Chicago in 2013. His current research interests include network biology, large-scale genomics, approximate inference and probabilistic graphical models. Jian is a recipient of Microsoft Research Fellowship (2010), Young Investigator Award in CROI (2011) and several best poster awards. His algorithms won the Crowdscale Challenge (2011), the Breast cancer cell line pharmacogenomics challenge (2011), the 2nd place in several CASP protein structure prediction experiments (2008, 2010, 2012) and selected as the most innovative method in CASP 2009 meeting.