Mining big data to comprehend complex diseases
Start: 01/05/2015 11:00 am
End: 01/05/2015 12:00 pm
Department of Computer Science
Hong Kong Baptist University
Many common human diseases, such as type-1 and type-2 diabetes, depression, schizophrenia, and prostate cancer, are influenced by several genetic and environmental factors. Scientists and public health officials have struggled to find genetic patterns associated with complex diseases, not only to advance our understanding of multi-gene disorders, but also to provide more insights into complex diseases. However, most of the genetic factors that have been identified contribute relatively small increments of risk and only explain a small portion of the genetic variation in complex diseases. As high-throughput data acquisition becomes popular in biomedical research, it is timely to propose some novel approaches to mining the large-scale genomic data to find new genetic patterns. In this talk, I will first introduce our previous contributions on detecting genetic interactions. Next, I will present our on-going works on the integrative analysis of multiple large-scale genomic data sets. Some preliminary results have shown that our approaches have greater power, less false positives, and more accurate estimations of genetic effects.
About the Speaker: Dr. Xiang Wan earned a Master of Science and a Ph.D. in computing science from the University of Alberta, Canada. He had previously worked as a post-doctoral fellow in Bioinformatics Lab at the University of British Columbia in 2007, and a research associate of the ECE department at the Hong Kong University of Science and Technology from November 2007 to September 2011. His work has been published in a variety of academic journals, including American Journal of Human Genetics, Nature Genetics, Bioinformatics, BMC Bioinformatics, IEEE/ACM Transactions on Computational Biology and Bioinformatics, Neuroinformatics, among others.
Department Conference Room (25 Park Place, Room 755)