生物统计学虚拟研讨会-Ali Shojaie博士在差异网络分析中
何时何地
扬uary 17
12:00 pm-下午1:15
Webex或RAS-811（在Google地图中查看)
接触
- 斯科特·戴森（Scott Dyson）
- scott.b.dyson@uth.tmc.edu
事件描述
主持人：
Ali Shojaie, PhD
生物统计学和统计学教授（辅助）
生物统计局副主席
University of Washington
地点：RAS E-811 if you wish to attend in person, otherwise WebEx. The presenter will be connecting via WebEx.
WebexLink: https://uthealth.webex.com/uthealth/j.php?MTID=m23d314e628d45d5aff4f1dd2699d398c
WebEx密码：KJRBPNGU957
抽象的
Recent evidence suggests that changes in biological networks, e.g., rewiring or disruption of key interactions, may be associated with development of complex diseases. These findings have motivated new research in computational and experimental biology that aim to obtain condition-specific estimates of biological networks, e.g. for normal and tumor samples, and identify differential patterns of connectivity in such networks, known as differential network analysis. In this talk, we primarily focus on testing whether two Gaussian graphical models are the same. We will first illustrate that existing inference procedures for this task may lead to misleading results. To address this shortcoming, we propose a two-step inference framework, for testing the null hypothesis that the edge sets in two networks are the same. The proposed framework is especially appropriate if the goal is to identify nodes or edges that show differential connectivity. Time permitting, we will also discuss how differential network analysis methods can be extended to non-Gaussian settings as well as settings where differences in network edges are functions of other covariates.
Event Site Link
https://uthealth.webex.com/uthealth/j.php?mtid=m23d314e628d45d45d555aff4f4f1dd269998c
附加信息
