Consistent model selection in the p>>n setting

p>>n 设置中一致的模型选择

• 批准号: 8084684
• 负责人: Valen EARL Johnson
• 金额: $ 31.03万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 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 2015-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8084684
• 关键词: Address; Algorithms; Area; Biochemical Genetics; Biological; Biological Markers; Classification; Clinical; Clip; Colorectal Cancer; Data; Data Set; Development; Disease; Disease Attributes; Disease Outcome; Ensure; Gene Expression; Genes; Goals; Lasso; Linear Models; Log-Linear Models; Malignant Neoplasms; Measurement; Measures; Medical; Medical Research; Methodology; Methods; Modeling; Molecular; Outcome; Pathway interactions; Patients; Plant Roots; Play; Probability; Procedures; Process; Property; Research Personnel; Role; Sample Size; Scheme; Selection Criteria; Series; Single Nucleotide Polymorphism; Space Models; Speed; Statistical Models; Techniques; Technology; Testing; base; density; discrete data; genome wide association study; high throughput screening; innovation; malignant breast neoplasm; research study; response; simulation; statistics; success; tool

DESCRIPTION (provided by applicant): Among the most fundamental and commonly encountered statistical problems in medical research is the problem of model selection. Model selection is the process by which researchers identify the relationships between measured quantities; thus it plays a central role in the analysis of essentially all high-throughput screening data. Model selection procedures represent the primary analytical mechanism through which the associations between diseases and large numbers of biochemical, genetic and pharmacological variables are discovered. The fundamental hypothesis tested in this application is that a new class of model selection procedures can be used to effectively identify associations between biological variables and disease outcomes, even in settings where there are many more potential biological correlates than there are observations on each variable. The goals of this project are to develop these variable selection procedures so that they can be applied to high-throughput screening data, and to apply the resulting methodology in three important application areas. To achieve these goals, the following specific aims will be addressed. Known theoretical properties of the proposed model selection procedures will be extended to cases in which there are many more biological measurements available than there are observations on each measurement (i.e., p n setting). Constraints on the number of variables that can be included in final models for outcome variables will be determined, and efficient numerical algorithms will be developed so that these methods can be applied to actual high-throughput screening data. The new model selection procedures will be used to define binary classification algorithms that can predict clinical outcomes from high-dimensional gene expression data sets. The new model selection procedures will be used to identify and analyze interactions between genes that are associated with cancer and other diseases in genome-wide association studies using single-nucleotide polymorphism data. The new model selection procedures will be used to analyze biological pathways as informed by high- throughput molecular interrogation data. The algorithms developed during this project constitute a major innovation in the field of model selection and will provide medical researchers with a new and unique set of tools for effectively identifying biological associations among biomarkers, disease attributes, and patient outcomes from high-throughput screening data. PUBLIC HEALTH RELEVANCE: Model selection procedures are statistical techniques that allow researchers to discover the associations between disease and the large number of variables that are measured in emerging high-throughput screening technologies. For example, model selection techniques are used to discover which genes are associated with particular forms of cancer. This project proposes a new class of model selection procedures that will make it easier for researchers to discover such associations.

描述（由申请人提供）：在医学研究中最基本和最常见的统计问题是模型选择问题。模型选择是研究人员确定测量量之间关系的过程;因此，它在基本上所有高通量筛选数据的分析中起着核心作用。模型选择程序代表了主要的分析机制，通过该机制发现疾病与大量生化、遗传和药理学变量之间的关联。 在本申请中测试的基本假设是，可以使用一类新的模型选择程序来有效地识别生物变量和疾病结果之间的关联，即使在存在比每个变量上的观察结果多得多的潜在生物学相关性的环境中。该项目的目标是开发这些变量选择程序，使其可以应用于高通量筛选数据，并将所得方法应用于三个重要的应用领域。为实现这些目标，将致力于实现以下具体目标。 所提出的模型选择过程的已知理论性质将被扩展到其中存在比关于每个测量的观察多得多的可用生物测量的情况（即，PN设置）。将确定结果变量最终模型中可以包含的变量数量的约束，并开发高效的数值算法，以便这些方法可以应用于实际的高通量筛选数据。 新的模型选择程序将用于定义二元分类算法，该算法可以从高维基因表达数据集预测临床结果。 新的模型选择程序将用于使用单核苷酸多态性数据在全基因组关联研究中识别和分析与癌症和其他疾病相关的基因之间的相互作用。 新的模型选择程序将用于分析生物途径，如高通量分子询问数据所告知的。在该项目中开发的算法构成了模型选择领域的一项重大创新，将为医学研究人员提供一套新的独特的工具，用于从高通量筛选数据中有效识别生物标志物，疾病属性和患者结局之间的生物学关联。 公共卫生相关性：模型选择程序是一种统计技术，使研究人员能够发现疾病与新兴高通量筛选技术中测量的大量变量之间的关联。例如，模型选择技术用于发现哪些基因与特定形式的癌症相关。该项目提出了一类新的模型选择程序，这将使研究人员更容易发现这种关联。