Data Analysis, Emerging Models, Computational Biology, Microbiome, Stritch School of Medicine, DNA, Genomics, Metagenomics, Public Health, National Science Foundation
One of the hottest topics in medical research these days is the microbiome, which studies the roles of microbial communities on human health and diseases. As in many other data intensive fields of biological sciences, data analysis remains as the bottleneck for microbiome research and novel computational tools must be developed to enable biological discoveries.
In this talk, Dr. Dong will first give a broad overview on the field of microbiome research and associated major computational challenges with microbiome data analysis.
Discussing a recently developed computational method for the fundamental challenge encountered in all the microbiome studies, i.e., how to accurately identify various bacterial species from high-throughput DNA sequencing data.
Qunfeng Dong, PhD, is the Director of the Center for Biomedical Informatics (http://ssom.luc.edu/cbmi/) and an associate professor in the Department of Public Health Sciences at the Stritch School of Medicine, Loyola University Chicago. After earning his PhD in biochemistry in 2000 from Iowa State University (ISU), he completed his postdoctoral training in computational biology in the ISU mathematics department by developing algorithms for protein structural computation.
From 2001 to 2006, he was a research scientist at ISU, working on NSF-funded projects to develop bioinformatics databases and analysis tools for genomics data.From 2006 to 2009, he served as the Bioinformatics Director of the Center for Genomics and Bioinformatics at Indiana University.
In 2010, he joined the University of North Texas (UNT) as an assistant professor in the Biological Sciences and Computer Science & Engineering departments, and he became an associate professor with tenure in 2014 at UNT. He joined the faculty of the Stritch School of Medicine in January of 2015.
His research interests include data analysis, developing computational tools for genomics and metagenomics research, and mining electronic medical records.