Consistent variable selection in p>>n settings

 描述（由申请人提供）：分子特征指导的临床治疗对于推进癌症治疗至关重要，最近可用于鉴定有助于癌症基因功能的突变、表达水平和甲基化（以及这些效应的组合）的分子数据类型数量激增。大量的此类数据现在可以在存储库中公开获得，如癌症基因组图谱项目和国际癌症基因组联盟，在那里等待统计分析。就像大海捞针一样，在分析这些数据时出现的中心问题是从大量的非预后因素中识别重要的预后因素。该项目的研究人员最近开发了一种新方法，可以完成这一壮举。他们的方法已被证明可以正确识别预测结果的重要因素，当可用于预测的因素比结果的观察结果多得多时，最近的理论发展和模拟研究表明，这些结果可以扩展到比癌症患者组织样本更多的可能基因表达值的情况。该项目的目标是扩展这些方法，使其可以应用于更广泛的患者结果数据类别，使这些方法在计算上更有效，使其可以常规应用于大规模基因组数据集，将这些方法应用于现有的癌症研究，并将这些新方法整合到软件工具中，分发给全世界的癌症研究人员，有效识别与癌症功能相关或预测新的或现有癌症疗法成功的基因突变。

 DESCRIPTION (provided by applicant): Molecular signature-guided clinical therapies are critical to advancing the treatment of cancer, and there has been a recent explosion in the number of types of molecular data that can potentially be used to identify mutations, expression levels, and methylations (and combinations of these effects) that contribute to cancer gene functioning. Vast stores of such data are now publicly available in repositories, like The Cancer Genome Atlas Projects and the International Cancer Genome Consortium, where they await statistical analyses. Like finding a needle in a haystack, the central problem that arises in the analyses of these data is the problem of identifying important prognostic factors from huge numbers of non-prognostic factors. The investigators of this project have recently developed a new method that can accomplish this feat. Their approach has proven to correctly identify important factors that predict outcomes when there are many more factors that can be used for prediction than there are observations of an outcome, and recent theoretical developments and simulation studies have demonstrated that these results can be extended to situations in which there are many, many more possible gene expression values than there are tissue samples from cancer patients. The goal of this project is to extend these methods so that they can be applied to broader classes of patient outcome data, to make these methods more computationally efficient so that they can be applied routinely to massive genomic datasets, to apply these methods to existing cancer studies, and to incorporate these new methods into software tools that can be distributed to cancer researchers throughout the world so that they can more effectively identify genetic mutations that are either associated with cancer functioning or predictive of the success of new or existing cancer therapies.