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: 7392341
• 负责人: Marina Vannucci
• 金额: $ 19.3万
• 依托单位: RICE 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 2011-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7392341
• 关键词: Acute; Adult; Adult Acute Lymphocytic Leukemia; Bayesian Method; Biological Markers; Code; Communities; Computer software; DNA Microarray Chip; DNA Microarray format; Data; Data Analyses; Degenerative polyarthritis; Development; Diagnosis; Dimensions; Discrimination; Disease; Event; Failure; Genes; Genomics; Goals; Lead; Methodology; Methods; Microarray Analysis; Modeling; Molecular; Numbers; Outcome; Patients; Proteomics; Research; Research Personnel; Research Proposals; Rheumatoid Arthritis; Sampling; Series; Techniques; Texas; Time; base; drug development; improved; interest; leukemia; novel; programs; response; software development; sound; statistics

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)：这项研究提案的总体目标是开发新的贝叶斯方法来分析基因组学中出现的数据。特别令人感兴趣的是有大量变量可用并且选择预测子集是目标之一的情况。我们提出的理论发展是由各种研究推动的，其中一些研究是由我们的生物医学合作者利用DNA微阵列技术进行的。这个项目的目标之一是在统计学中的变量和特征选择方面贡献新的理论发展。另一个目标是为生物医学界提供分析高维数据的可靠方法。重要生物标志物的识别将有助于更好地了解特定疾病涉及的分子机制，并反过来将改善患者的诊断、药物开发和治疗。我们建议的研究的具体目标是： 1.高维数据的聚类：我们将开发新的贝叶斯方法，同时对实验单元进行聚类，并确定最能区分不同组的变量。 2.具有删失生存结果的高维数据分析：我们将研究参数生存模型中变量选择的新方法。这些方法将导致生存估计和预测变量的识别。 3.在微阵列研究中的应用：我们将把特定目标#1和#2的方法应用于一系列涉及微阵列数据的生物医学研究。这些研究包括类风湿性关节炎、骨关节炎和成人急性淋巴细胞性白血病。 4.在蛋白质组数据中的应用：我们将结合降维小波技术，将我们的方法应用于蛋白质组数据中的重要特征提取问题。 5.软件开发：我们将开发统计软件，并向公众开放。