報(bào)告題目：Node-based differential network analysis in genomics
主講嘉賓：歐陽(yáng)樂(lè)老師
主持人：賈紅老師
時(shí)間：2017年4月13日（星期四）16:00
地點(diǎn)：深圳大學(xué)南區(qū)基礎(chǔ)實(shí)驗(yàn)樓北座信息工程學(xué)院N710會(huì)議室
內(nèi)容簡(jiǎn)介：
Gene dependency networks often undergo changes in response to different conditions. Understanding how these networks changeacross two conditions is an important task in genomics research. Most of previous differential network analysis approaches assumethat the difference between two condition-specific networks is driven by individual edges. Thus, they will fail in detecting keyplayers which might represent important genes whose mutations drive the change of network. In this work, we develop a node-baseddifferential network analysis (N-DNA) model to directly estimate the differential network whichis driven by certain hub nodes.We model each condition-specific gene network as a precision matrix and the differential network as the difference between twoprecision matrices. Then we formulate a convex optimization problem to infer the differential network by combing a D-trace lossfunction and a row-column overlap norm penalty function. Simulation studies demonstrate that N-DNA provides more accurateestimate of the differential network than previous competing approaches. We apply N-DNA to ovarian cancer and breast cancergene expression data. The model rediscovers known cancer-related genes and contains interesting predictions.
