Jian Peng - Assistant Professor, Computer Science
University of Illinois Urbana-Champaign
Abstract:
Recent advances in network and functional genomics have enabled large-scale measurements of molecular interactions, functional behavior and consequences of genetic perturbations. Identifying connections, patterns and deeper functional annotations among such heterogeneous measurements will enhance our capability to prediction proteins' function, discover their roles in biological processes underlying diseases, and develop novel therapeutics. In this talk, I will describe machine algorithms that interrogate molecular interactions and perturbation screens to understand protein functions. First, I will introduce Mashup, a graph-based learning algorithm that integrates multiple heterogeneous networks into compact topological features for protein functional inference. I will also briefly talk about applications of Mashup to discovering new disease factors and subnetworks from genetic perturbations and variations. Finally, I will present our recent work on using deep learning for modeling protein sequence-to-function mapping from large-scale mutagenesis and its application to protein design and engineering.
Biography:
Jian Peng has been an assistant professor of computer science at UIUC since 2015. Before joining Illinois, Jian was a postdoc at CSAIL at MIT and a visiting scientist at the Whitehead Institute for Biomedical Research. He obtained his Ph.D. in Computer Science from Toyota Technological Institute at Chicago in 2013. His research interests include bioinformatics, cheminformatics and machine learning. Algorithms developed by Jian and his co-workers were successful in several scientific challenges, including the Critical Assessment of Protein Structure Prediction (CASP) competitions and a few DREAM challenges on translational medicine and pharmacogenomics. Recently, Jian has received an NSF CAREER Award, a PhRMA Foundation Award, and an Alfred P. Sloan Research Fellowship.
