Heterogeneous and Robust Survival Analysis in Genomic Studies

项目描述（由申请人提供）：该项目的长期目标是开发强大且计算效率高的统计方法，用于由重要生物学问题和实验驱动的高维基因组数据的统计建模。当前项目的具体目标包括开发新的生存分析方法来模拟基因组研究中患者和生物标志物的异质性，以及开发强大的生存分析方法来分析高维基因组数据。所提出的方法取决于高维数据分析方法、统计学习理论和人类基因组学方法的新整合。该项目还将调查这些方法的健壮性、功率和效率，并将它们与现有方法进行比较。将该方法应用于卵巢癌、肺癌、脑癌的研究结果将有助于确保从我们的合作者进行的高通量实验以及公开可用的数据中获得最大的信息。软件将通过Bioconductor提供，以确保科学界从开发的方法中受益。

DESCRIPTION (provided by applicant): The long-term objective of this project is to develop powerful and computationally-efficient statistical methods for statistical modeling of high-dimensional genomic data motivated by important biological problems and experiments. The specific aims of the current project include developing novel survival analysis methods to model the heterogeneity in both patients and biomarkers in genomic studies and developing robust survival analysis methods to analyze high-dimensional genomic data. The proposed methods hinge on a novel integration of methods in high-dimensional data analysis, theory in statistical learning and methods in human genomics. The project will also investigate the robustness, power and efficiencies of these methods and compare them with existing methods. Results from applying the methods to studies of ovarian cancer, lung cancer, brain cancer will help ensure that maximal information is obtained from the high-throughput experiments conducted by our collaborators as well as data that are publicly available. Software will be made available through Bioconductor to ensure that the scientific community benefits from the methods developed.