Title: The large-scale analysis of genomic data in complex diseases

Speaker: 万翔博士（深圳市大数据研究院）

Time: 7月24日上午9:30-10:30

Place：青岛校区304am永利集团官网N3楼332会议室

Abstract:

Many common human diseases, such as type-1 and type-2diabetes, 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 in the study of complex diseases.

Short Bio:

万翔博士本科毕业于中国人民大学信息学院、博士毕业于加拿大艾伯塔大学，在加拿大大不列颠哥伦比亚大学做过博后，在香港浸会大学做过研究助理教授。目前是深圳市大数据研究院的研究科学家。万翔博士一直从事生物医学大数据的模式识别工作，包括NMR中的蛋白结构预测、基因表达和全基因组关联分析，在运用人工智能和机器学习方法分析大生物数据有丰富的研究经验并且取得丰富的研究成果。他已在国外知名刊物上发表了将近40多篇期刊文章，包括国际顶尖基因学杂志Nature Genetics，American Journal of Human Genetics，BMC Genetics，国际权威分子生物信息学杂志Bioinformatics，BMC Bioinformatics，Neuro informatics，IEEE/ACM Transactions on Computational Biology and Bioinformatics。已开发的软件，例如BOOST，GBOOST 正在受到越来越多的关注和使用。其中，自BOOST方法2010年10月发表在American Journal of Human Gentics 以来，BOOST软件已经被下载1400多次。自2013年以来的文章引用次数超过1500次。