报告时间： 星期五（2015.7.10）3：30pm
报告地点： 博习楼327
报告人：Li Shen Ph.D.，Associate Professor， Indiana University School of Medicine
邀请人： 陈新建 特聘教授
报告摘要：Today's generation of multi-modal imaging systems produces massive high dimensional data sets, which when coupled with high throughput genotyping and sequencing data, provide exciting opportunities to enhance our understanding of phenotypic characteristics and the genetic architecture of human diseases. However, the unprecedented scale and complexity of these data sets have presented critical bottlenecks requiring new concepts and enabling tools. In this talk, using the quantitative genetics study of the Alzheimer's Disease Neuroimaging Initiative (ADNI) data as an example, we discuss the recent development of bioinformatics strategies for multidimensional brain imaging genomics. We review and synthesize ADNI genetic associations with disease status or quantitative disease endophenotypes including structural and functional neuroimaging, fluid biomarker assays, and cognitive performance; and discuss the diverse analytical strategies used in these studies. We show that the broad availability and wide scope of ADNI genetic and phenotypic data has advanced our understanding of the genetic basis of AD and has nominated novel targets for future studies.
报告人简介：
Dr. Li Shen holds a B.S. degree from Xi'an Jiao Tong University, an M.S. degree from Shanghai Jiao Tong University, and a Ph.D. degree from Dartmouth College, all in Computer Science. He is an Associate Professor of Radiology and Imaging Sciences at Indiana University School of Medicine (IUSM), and Associate Director of IU Center for Computational Biology and Bioinformatics (CCBB). He is an elected member of the MICCAI Society Board of Directors. He is a member of the Center for Neuroimaging, the Center for Computational Biology and Bioinformatics, and the Stark Neurosciences Research Institute at IUSM. He is also affiliated with Departments of CIS, ECE and Biostatistics, and School of Informatics and Computing. His research interests include medical image computing, bioinformatics, data mining, and network science. The central theme of his lab is focused on developing computational and informatics methods for integrative analysis of multimodal imaging data, high throughput "omics" data, fluid and cognitive biomarker data, and rich biological knowledge such as pathways and networks, with applications to various complex disorders. The ultimate goal is to improve mechanistic understanding of disease processes and treatment responses for early diagnosis and therapeutics. His research is primarily funded by NIH (NLM, NIA, NIBIB, NIAAA, NCATS), NSF, DOD and NCAA. Further information about Dr. Shen's research activities is available at http://www.iu.edu/~shenlab/.