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学院系列学术报告(一)
发布日期:2018-06-26 浏览次数:

报告题目Challenges and Progress in Imaging Genetic – Big Data Squared Studies

报告人:张和平

报告摘要Neuroimaging has been an essential tool for collecting data on the functioning of brain. High throughput technologies have provided ultra-dense genetic markers to enable us in identifying genetic variants for complex diseases. Only until recently, datasets of reasonably large scale become available that contain both imaging and genetic data. Due to the complexities and high dimensionality in such data, most of the existing datasets are still relatively small in sample sizes but larger datasets are in the horizon. Thus, it is timely and important to develop statistical methods and analytic tools to analyze imaging genetic data – the so-called big data squared. In this talk, I will present the basic technologies, concepts, challenges, and methods related to imaging genetic data. I will use specific data on learning disorders illustrate how to quantify neurobiological risk for learning problems with neuroimaging biomarkers and how to integrate imaging and genetic data in our understanding of cognition and genetic etiologies.

报告时间7月1日上午9:00-9:30

报告地点: 科技楼二楼北会议室

报告人简介: 张和平,耶鲁大学教授。国家千人计划(B类)入选者,长江学者讲座教授。主要从事数理、遗传学、生物信息、流行病学与卫生学、儿科学等方面的研究。个人主页:https://publichealth.yale.edu/people/heping_zhang.profile


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