报告主题： 系统生物学中的组稀疏优化研究
报告人：胡耀华 副教授 （深圳大学）
报告时间：2019年4月18日（周四）15：00
报告地点：校本部G507
邀请人：徐 姿
主办部门：太阳成集团tyc33455数学系
报告摘要：Inferring gene regulatory networks from gene expression data and identifying key factors for cell fate conversion are two arduous challenges in biology and regenerative medicine, especially in higher organisms (like human and mouse) where the number of genes is large but the number of experimental samples is small. In this talk, we will formulate these two systems biology problems into group sparse optimization problem by employing the special structure of the involved regulatory networks. The lower-order regularization method for group sparse optimization will be introduced in a unified framework. Theoretical guarantee of the lower-order regularization method is provided via the oracle property and recovery bound, and the numerical performance of the proximal gradient algorithm is presented via the linear convergence property. The applications of group sparse optimization will facilitate biologists to study the gene regulation of higher model organisms in a genome-wide scale.
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