Adaptive Methodology for Functional Biomedical Data

功能生物医学数据的自适应方法

• 批准号: 7778328
• 负责人: JEFFREY S MORRIS
• 金额: $ 25.4万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-03-01 至 2012-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7778328
• 关键词: Accounting; Alcoholism; Bayesian Method; Biological; Biological Process; Biomedical Research; Brain; Cations; Code; Complex; Computer Simulation; Data; Experimental Designs; Genomics; Goals; Image; Individual; Malignant Neoplasms; Measurement; Methodology; Methods; Modeling; Neurologic; Opiate Addiction; Ovarian; Principal Component Analysis; Process; Proteomics; Research; Sampling; Series; Source; Space Perception; Statistical Methods; Structure; Surface; Tail; Testing; Work; base; chemotherapy; flexibility; malignant breast neoplasm; programs; public health relevance; research study; response; software development; technological innovation

DESCRIPTION (provided by applicant): An ever-increasing number of biomedical studies yield functional data sampled on a fine grid. These type of data are frequently high dimensional and complex with many irregular features like peaks and change points. There is currently a dearth of existing rigorous statistical methods for analyzing this type of data. The goal of this research program is to develop new Bayesian methodology that provides a unified framework for modeling and performing inference on samples of curves that is flexible enough to apply to a variety of applications, from various experimental designs, and can answer a broad range of research questions. 1. We will develop new methodology within the wavelet-based functional mixed model framework that accommodates outlying curves, a broader class of within- curve covariance structures, and higher dimensional functional data, making it applicable to a broad range of functional data. 2. We will develop methods to classify individuals based on their functional data, e.g. proteomic profiles, in a way that allows us to combine information across functional and scalar factors of multiple sources. We will develop methods to perform Bayesian functional hypothesis testing. 3. We will develop adaptive methods for relating functional predictors to functional responses. 4. We will develop methods for adaptive functional principal components analysis and for principal component-based functional mixed models, which represents a data-driven modeling framework that is extremely flexible in taking into account the complex structure that may be present in the functional data. 5. We will apply the methods to a number of cancer-related studies yielding functional data, including various types of proteomics and genomics data. 6. We will develop efficient, easy-to-use, freely available code to fit the methods described in this proposal.

描述（由申请人提供）：越来越多的生物医学研究产生了在细网格上采样的功能数据。这些类型的数据通常是高维且复杂的，具有许多不规则特征，例如峰和变化点。目前，缺乏用于分析此类数据的现有严格统计方法。该研究计划的目的是开发新的贝叶斯方法，该方法为建模和推断曲线样本提供了一个统一的框架，该曲线具有足够灵活的曲线样本，可以适用于各种实验设计的各种应用程序，并可以回答广泛的研究问题。 1。我们将在基于小波的功能混合模型框架内开发新的方法，该模型框架可容纳外围曲线，更广泛的曲线内协方差结构以及更高的功能数据，使其适用于广泛的功能数据。 2。我们将根据个人的功能数据开发分类的方法，例如蛋白质组织曲线，使我们能够在多个来源的功能和标量因子之间结合信息。我们将开发进行贝叶斯功能假设检验的方法。 3。我们将开发自适应方法将功能预测因子与功能响应联系起来。 4。我们将开发用于自适应功能主组件分析和基于主组件的功能混合模型的方法，该模型代表了数据驱动的建模框架，该框架非常灵活地考虑了功能数据中可能存在的复杂结构。 5。我们将将这些方法应用于许多与癌症相关的研究，这些研究产生了功能数据，包括各种类型的蛋白质组学和基因组学数据。 6。我们将开发高效，易于使用，免费提供的代码，以适合本提案中描述的方法。