Consistent variable selection in p>>n settings

p>>n 设置中一致的变量选择

• 批准号: 9340069
• 负责人: Valen EARL Johnson
• 金额: $ 32.11万
• 依托单位: TEXAS A&M UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-04-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9340069
• 关键词: Algorithms; Application procedure; Biocompatible Materials; Cancer Center; Cancer Etiology; Cancer Patient; Cancer Prognosis; Clinical; Collection; Communities; Computer software; Computing Methodologies; DNA; DNA Sequence Alteration; Data; Data Analyses; Data Set; Databases; Development; Dimensions; Disease; Employee Strikes; Environment; Explosion; Funding; Gene Expression; Genes; Genomics; Goals; International; Linear Models; Malignant Neoplasms; Malignant neoplasm of lung; Malignant neoplasm of ovary; Measures; Medical Research; Methodology; Methods; Methylation; Modeling; Molecular; Molecular Profiling; Mutation; Oncogenes; Outcome; Patient-Focused Outcomes; Patients; Pharmacotherapy; Predictive Factor; Procedures; Prognostic Factor; RNA methylation; Research Personnel; Resource Allocation; Resources; Sampling; Scheme; Shotguns; Site; Software Tools; Statistical Algorithm; Statistical Data Interpretation; Statistical Models; Techniques; Technology; Testing; The Cancer Genome Atlas; Time; Tissue Sample; Treatment Efficacy; Treatment outcome; Update; Validation; base; cancer genome; cancer genomics; cancer therapy; computer framework; experimental study; gene function; genomic data; high throughput screening; malignant breast neoplasm; model design; novel strategies; open source; outcome prediction; predictive modeling; prognostic; public health relevance; repository; screening; simulation; stem; success; theories

 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.

 描述(申请人提供)：分子签名指导的临床疗法对推进癌症治疗至关重要，最近可用于识别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)的分子数据类型出现了爆炸性增长。现在，在癌症基因组图谱项目和国际癌症基因组联盟等储存库中，可以公开获得大量这样的数据，这些数据正在等待统计分析。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非预测因素中识别重要的预测因素的问题。该项目的研究人员最近开发了一种可以完成这一壮举的新方法。他们的方法已经证明，当可用于预测的因素比对结果的观察要多得多时，他们的方法可以正确地识别预测结果的重要因素，并且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有更多可能的基因表达值的情况。该项目的目标是将这些方法扩展到更广泛的患者结果数据类别，使这些方法在计算上更加高效，以便它们可以常规地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，并将这些新方法合并到软件工具中，可以分发给世界各地的癌症研究人员，以便它们能够更有效地识别与癌症功能相关的基因突变，或者预测新的或现有的癌症治疗方法的成功。