Speaker:Fengzhu Sun, University of Southern California, USA
Date:June 24, 2019
Location:B1032 Lecture Hall, Zhixin Building, Central Campus
Sponsor:School of Mathematics
Markov random fields (MRF) have been widely used in many different fields including image analysis and theoretical physics. We previously used MRF to predict protein function based on protein interaction networks and individual features with excellent performance. High-throughput metagenomic sequencing technologies have profoundly increased our ability to identify viral genomic sequences without isolation. With the discovery of new viruses, one of the most fundamental challenges is to predict their hosts. We recently developed an integrated MRF model for predicting virus-host interactions based on networks for virus-virus similarity, virus-host similarity, and known virus-host interactions. We used the integrated model to predict hosts of viruses in many different environments yielding important insights into virus-host interactions.
Fengzhu Sun is a Professor of Molecular and Computational Biology. His Bachelors in Mathematics is from Shandong University, Masters in Probability and Statistics is from Peking University, and PhD in Applied Mathematics is from University of Southern California. His recent research interests include protein interaction networks, gene expression, single nucleotide polymorphisms (SNP), linkage disequilibrium (LD) and their applications in predicting protein functions, gene regulation networks, and disease gene identification. He is also interested in metagenomics, in particular, marine genomics. His previous research accomplishments include: 1) protein domain interaction and protein function prediction integrating multiple data sources, 2) dynamic programming algorithms for haplotype block partition and tag SNP selection, 3) 1-TDT: transmission disequilibrium test when one parent is available, and 3) theoretical studies of polymerase chain reaction (PCR) related biotechnologies.
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Edited by: Qu Xilin