Consistent variable selection in p>>n settings

p>>n 设置中一致的变量选择

• 批准号: 9106867
• 负责人: Valen EARL Johnson
• 金额: $ 33.32万
• 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/9106867
• 关键词: Algorithms; Application procedure; Bayesian Method; 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; Disease; Employee Strikes; Environment; Explosion; Funding; Gene Expression; Genes; Genomics; Goals; Health; International; Linear Models; Malignant Neoplasms; Malignant neoplasm of lung; Malignant neoplasm of ovary; Measures; Medical Research; Methods; Methylation; Modeling; Molecular; Molecular Profiling; Mutation; Oncogenes; Outcome; Patient-Focused Outcomes; Patients; Pharmacotherapy; Procedures; Prognostic Factor; RNA methylation; Research Personnel; Resource Allocation; Resources; Sampling; Scheme; Shotguns; Site; Software Tools; Statistical Algorithm; Statistical Models; Techniques; Technology; Testing; The Cancer Genome Atlas; Time; Tissue Sample; Treatment outcome; Update; Validation; base; cancer genome; cancer genomics; cancer therapy; computer framework; gene function; genomic data; high throughput screening; malignant breast neoplasm; model design; novel strategies; open source; predictive modeling; prognostic; repository; research study; 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.