报告题目: Multi-level heterogeneous omics data integration with kernel fusion

报告人：杨海涛 副教授

报告时间：2019年6月14日（周五）10:00

报告地点：逸夫楼C座314会议室

摘要：

High-throughput omics data are generated almost with no limit nowadays.Since the relationship among different omics data features are typically unknown, a supervised learning model assuming a particular distribution with a specific structure will not serve the purpose to capture the underlying complex relationship between multiple features and a disease phenotype. We proposed a fused KPLS (fKPLS) model for disease classification and prediction with multilevel omics data. The fused kernel can deal with effect heterogeneity in which different omic data types may have different effect contribution to the trait of interest, with the purpose to improve the prediction performance. We proposed to optimize the kernel parameters and kernel weights with the genetic algorithm (GA). The proposed GA-fKPLS model can substantially improve disease classification performance by integrating multiple omics data types, demonstrated via extensive simulations and real data analysis.

报告人简介：

杨海涛，卫生统计学博士，现任河北医科大学流行病与卫生统计学教研室 副教授。美国密歇根州立大学统计与概率系 博士后。中国现场统计研究会大数据统计分会理事，中国青年统计学家协会理事。研究兴趣包括统计遗传学与基因组学以及下一代基因序列数据分析等。目前，主持国家自然科学基金（面上）1项，以第一作者在BRIEFINGS IN BIOINFORMATICS发表SCI论文2篇。目前主要从事高维统计推断和多组学数据整合方法的研究。