学术报告报告主题: mirPLS: a partial linear structure identifier method for disease subtyping using MicroRNAs
报 告 人: 梁华
报告时间：2026年 6月11日（星期四）上午10:00-11:00
报告地点：4318会议室
邀 请 人: 刘卉灵副教授
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数学学院
2026年6月4日
报告摘要：
MicroRNAs (miRNAs) are small non-coding RNAs that have been successfully identified to be differentially expressed in various diseases. However, some miRNAs were reported to be up-regulated in one subtype of a disease but down-regulated in another, making overall associations between these miRNAs and the heterogeneous disease non-linear. These non-linearly associated miRNAs, if identified, are thus informative biomarkers for disease subtyping. Here, we propose mirPLS, a Partial Linear Structure identifier for miRNA data that simultaneously identifies miRNAs of linear or non-linear associations with diseases, when non-linearly associated miRNAs can then be used for subsequent disease subtyping. Simulation studies showed that mirPLS can identify both non-linearly and linearly outcome-associated miRNAs more accurately than the comparison methods. Using the identified non-linearly associated miRNAs subsequently improves the disease subtyping accuracy. Applications to miRNA data of three different cancer types suggest that the cancer subtypes defined by the non-linearly associated miRNAs identified by mirPLS are consistently more predictive of patient survival.
报告人介绍：
梁华，乔治•华盛顿大学统计和生物统计教授, 曾任美国罗切斯特大学医学院教授. 研究兴趣包括统计模型选择与模型平均、非参数与半参数回归、爱兹病临床试验与动态建模等。出版英文学术著作2本，在AoS, Biometrika, JASA, JRSSB发表20多篇学术论文。主持过9项美国国家科学基金会(NSF)以及美国国立卫生研究院(NIH)的研究项目。美国统计学会（ASA）会士(fellow)、国际数理统计学会（IMS）会士、 曾任JASA 等刊物的编委或副主编。