学术报告:Statistical Inference of Deleterious Genetic Variants from Functional and Population Genomic Data

报告题目:Statistical Inference of Deleterious Genetic Variants from Functional and Population Genomic Data

主讲:黄一飞 博士, 美国冷泉港实验室Population Genomic Data

日期:2017年4月24日(周一)

时间:上午10:00-12:00

地点:中山大学东校园数据科学与计算机学院A201

主持:郑伟诗 教授

 

Abstract:

Millions of genetic variants have been identified in the human genome, but it is challenging to understand the functional, clinical, and evolutionary significance of these variants. I am interested in solving this problem using computational methods. By combing state-of-the-art machine learning techniques and population genetic theory, I recently developed two novel statistical models, LINSIGHT and DeepINSIGHT, to infer deleterious genetic variants from functional and population genomic data. These new models are powerful both for prioritizing disease variants and for obtaining insights into natural selection.

Bio:

黄一飞,美国冷泉港实验室博士后,主要研究生物信息学、贝叶斯统计、机器学习等。2014年毕业于加拿大麦克马斯特大学并获博士学位。毕业后先后在加拿大不列颠哥伦比亚大学和美国冷泉港实验室从事博士后研究工作。近五年黄一飞博士在Nature Genetics,Bioinformatics,PLoS Computational Biology等学术杂志上发表第一作者论文多篇。其中2017年发表于权威学术杂志Nature Genetics上的LINSIGHT统计学模型已被Nature Reviews Genetics、BioCentury Innovations、KAIMRC Innovations等杂志和媒体广泛报导。