a predictive framework for integrating disparate genomic data types using sample-specific gene set enrichment analysis and multi-task learning预测框架集成不同的基因组数据类型使用sample-specific基因集富集分析和多任务学习.pdfVIP

A Predictive Framework for Integrating Disparate Genomic Data Types Using Sample-Specific Gene Set Enrichment Analysis and Multi-Task Learning 1,2 2 1 . 2 . Brian D. Bennett , Qing Xiong , Sayan Mukherjee * , Terrence S. Furey * 1 Departments of Statistical Science, Computer Science, and Mathematics, Institute for Genome Sciences and Policy, Duke University, Durham, North Carolina, United States of America, 2 Department of Genetics, Department of Biology, Lineberger Comprehensive Cancer Center, and Carolina Center for Genome Sciences, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America Abstract Understanding the root molecular and genetic causes driving complex traits is a fundamental challenge in genomics and genetics. Numerous studies have used variation in gene expression to understand complex traits, but the underlying genomic variation that contributes to these expression changes is not well understood. In this study, we developed a framework to integrate gene expression and genotype data to identify biological differences between samples from opposing complex trait classes that are driven by expression changes and genotypic variation. This framework utilizes pathway analysis and multi-task learning to build a predictive model and discover pathways relevant to the complex trait of interest. We simulated expression and genotype data to test the predictive ability of our framework and to measure how well it uncovered pathways with genes both differentially expressed and genetically associated with a complex trait. We found that the predictive performance of the multi-task model was comparable to other similar methods. Also, methods like mult