学术报告：基于稀疏概率图模型的基因网络扰动分析

报告摘要：Understanding how gene regulatory networks change under different cellular states is important for revealing insights into network dynamics. Gaussian graphical models, which assume that the data follow a joint normal distribution, have been used recently to infer differential networks. However, the distributions of the omics data are non-normal in general. Furthermore, although much biological knowledge (or prior information) has been accumulated, most existing methods ignore the valuable prior information. Therefore, new statistical methods are needed to relax the normality assumption and make full use of prior information. We propose a new differential network analysis method to address the above challenges. Instead of using Gaussian graphical models, we employ a non-paranormal graphical model that can relax the normality assumption. We develop a principled model to take into account the following prior information: (i) a differential edge less likely exists between two genes that do not participate together in the same pathway; (ii) changes in the networks are driven by certain regulator genes that are perturbed across different cellular states and (iii) the differential networks estimated from multi-view gene expression data likely share common structures. Simulation studies demonstrate that our method outperforms other graphical model-based algorithms. We apply our method to identify the differential networks between platinum-sensitive and platinum-resistant ovarian tumors, and the differential networks between the proneural and mesenchymal subtypes of glioblastoma. Hub nodes in the estimated differential networks rediscover known cancer-related regulator genes and contain interesting predictions.

主办单位：英国立博官网中文版

报告专家：张晓飞，华中师范大学

报告时间：2018年6月1日（星期五）上午9：30

报告地点：学院201报告厅

专家简介：张晓飞，博士，华中师范大学英国立博官网中文版副教授，硕士研究生导师。主要从事基于机器学习方法的大规模医学组学数据挖掘研究。曾主持国家自然科学基金青年项目1项,参与国家重点研发计划“精准医学研究”重点专项1项，参与国家自然科学基金重点项目1项。已在Bioinformatics、BMC Bioinformatics、BMC Genomics、IEEE/ACM Transactions on Computational Biology and Bioinformatics等生物信息领域重要期刊发表论文26篇,累计影响因子80左右,论文被引用350余次（谷歌学术）。