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(4月19日) Statistical Inference of Deleterious Genetic Variants from Functional and Population Genomic Data
来源:  作者:  发表时间:2017-04-17  阅读次数:882次

报告人:黄一飞博士

主持人:高绍荣教授

时    间:4月19日上午10:30

地    点:医学楼1102室

 

Research Focus

     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.

Employment and Education

Ø  2015.6-present    Postdoctoral fellow, Cold Spring Harbor Laboratory, USA

Ø  2014.5-2015.2     Postdoctoral fellow, University of British Columbia, Canada

Ø  2009.9-2014.5     PhD in Bioinformatics, McMaster University, Canada

Ø  2006.9-2009-7    MSc in Bioinformatics, Beijing Normal University, China

Ø  2002.9-2006-7    BSc in Biotechnology, Zhengzhou University, China

Selected Publications

Ø  1. Yi-Fei Huang, Brad Gulko & Adam Siepel. Fast, scalable prediction of deleterious noncoding variants from functional and population genomic data, Nature Genetics, 2017

Ø  3. Yi-Fei Huang & G. Brian Golding. FuncPatch: A web server for the fast Bayesian inference of conserved functional patches in protein 3D structures. Bioinformatics, 2015

Ø  4. Yi-Fei Huang & G. Brian Golding.Phylogenetic Gaussian process model for the inference of functionally important regions in protein tertiary structures. PLoS Computational Biology, 2014

Ø  5. Yi-Fei Huang & G. Brian Golding. Inferring sequence regions under functional divergence in duplicate genes. Bioinformatics, 2012

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