学术报告报告题目：A Structured sparse optimization in systems biology
报 告 人：胡耀华 副教授
报告时间：2019年9 月19 日（星期四上午）11:00-12:00
报告地点：4号楼318 室
邀 请 人：潘少华 教授
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数学学院
2019年9月8日
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 structured sparse optimization problem by employing the special structure of the involved regulatory networks. The lower-order regularization method for structured 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.