Computational Biology
Members of our group study the use of computational methods to deal with large, complex data sets generated by high-throughput DNA, RNA and protein sequencing methods. This research involves the development, implementation and use of new algorithms, databases and high performance computing to solve problems related to human health. The following topics are the current foci for this group.
 
Geographic Distribution of Human Genetic Variation
This research involves the understanding the genetic etiology of Mendelian and complex traits; how human population history and cultural practices influence patterns of genetic variation; and the ways in which these patterns can be harnessed to advance the discovery of genes that underlie human disease. It is more broadly interested in understanding how the geographic distribution of human genetic variation relates to the susceptibility of different populations to disease, and ultimately how this variation in disease susceptibility reflects the evolutionary history of human populations.

Large-Scale Machine Learning for Genome and Transcriptome Analysis
This team targets the development of large-scale machine learning and statistical algorithms to combine different layers of multimodal genomic data computationally to form complex models, which incorporate expert biological knowledge and predict biological outcomes based on the models in a context-dependent manner. The goal is to help clinicians and biologists gain new insights and to better understand disease mechanisms and to improve diagnosis and treatment.

Biomedical Proteomics
Proteomics is the simultaneous, detailed study of all of the proteins made by an organism. The research in this team develops new ways to examine very large sets of results from proteomics experiments and to use data about protein and gene variations derived from previous experimental results to inform the analysis of new results. Issues of clinical concern are central themes, such as reproducibility, quality control and confidence levels.

Faculty in this research area:
Dr. Pingzhao Hu
Dr. Michelle Liu
Dr. Trevor Pemberton
Dr. Wayne Xu