Bayesian Methods for Genomics with Variable Selection

具有变量选择的基因组学贝叶斯方法

• 批准号: 7046119
• 负责人: Marina Vannucci
• 金额: $ 19.95万
• 依托单位: TEXAS A&M UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-04-01 至 2009-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7046119
• 关键词: acute lymphocytic leukemia; biotechnology; computer program /software; computer system design /evaluation; functional /structural genomics; high throughput technology; human data; information retrieval; mathematical model; microarray technology; model design /development; molecular biology information system; molecular genetics; nucleic acid sequence; proteomics; rheumatoid arthritis; statistics /biometry; acute lymphocytic leukemia; biotechnology; computer program /software; computer system design /evaluation; functional /structural genomics; high throughput technology; human data; information retrieval; mathematical model; microarray technology; model design /development; molecular biology information system; molecular genetics; nucleic acid sequence; proteomics; rheumatoid arthritis; statistics /biometry

DESCRIPTION (provided by applicant): The overall objective of this research proposal is to develop new Bayesian methodologies for the analysis of data that arise in genomics. Of particular interest are situations where a large number of variables is available and selection of a predictive subset is one of the goals. The theoretical developments we propose are motivated by a variety of studies, some conducted by our biomedical collaborators, using DNA microarray technologies. One of the goals of this project is to contribute novel theoretical developments in variable and feature selection in statistics. Another goal is to provide the biomedical community with sound methods for the analysis of high-dimensional data. The identification of important biomarkers will provide a better understanding of the molecular mechanisms involved in specific diseases, and will in turn improve diagnosis, drug development, and treatment of patients.The specific aims of our proposed research are: 1. Clustering of High-Dimensional Data: We will develop novel Bayesian methods for simultaneously clustering experimental units and identifying the variables that best discriminate the different groups. 2. Analysis of High-Dimensional Data with Censored Survival Outcomes: We will investigate novel methods for variable selection in parametric survival models. The methods will lead to estimates of the survival and to the identification of the predictive variables. 3. Application to Microarray Studies: We will apply the methods of Specific Aims #1 and #2 to a series of biomedical studies involving microarray data. These include studies on rheumatoid arthritis and osteoarthritis and adult acute lymphobiastic leukemia. 4. Application to Proteomic Data: We will adapt our methodologies to the problem of extracting important features in proteomics data, incorporating dimension reduction wavelet techniques. 5. Software development: We will develop statistical software and will make it available to the public.

描述（由申请人提供）：该研究建议的总体目标是开发新的贝叶斯方法，以分析基因组学中出现的数据。特别令人感兴趣的是可以使用大量变量的情况，并且选择预测子集的选择是目标之一。我们提出的理论发展是由多种研究的动机，其中一些是由我们的生物医学合作者使用DNA微阵列技术进行的。该项目的目标之一是在统计数据中为变量和特征选择做出新的理论发展。另一个目标是为生物医学界提供高维数据分析的合理方法。重要的生物标志物的鉴定将更好地理解对特定疾病所涉及的分子机制，进而改善诊断，药物发育和患者的治疗。我们拟议的研究的具体目的是： 1。高维数据的聚类：我们将开发新型的贝叶斯方法，用于同时聚类实验单元并确定最能区分不同组的变量。 2。分析具有审查生存结果的高维数据：我们将研究参数生存模型中可变选择的新方法。这些方法将导致对预测变量的生存和鉴定的估计。 3。在微阵列研究中的应用：我们将应用特定目标＃1和2的方法，涉及涉及微阵列数据的一系列生物医学研究。其中包括有关类风湿关节炎和骨关节炎以及成人急性淋巴细胞性白血病的研究。 4。应用于蛋白质组学数据：我们将使我们的方法学适应蛋白质组学数据中提取重要特征的问题，并结合了降低小波技术。 5。软件开发：我们将开发统计软件，并将其向公众提供。