Analysis of Microarray Gene Expression of Tumor

描述(由申请人提供)： 由当前微阵列技术产生的基因表达数据是一种 对人类生物学的深厚知识和洞察力的潜在来源 条件。微阵列数据是大量事实和数字的集合， 必须进行组织、总结、建模、分析和解释，以产生 有用的结论。这项拟议的研究计划的目标是开发 统计思想和方法，将使有用的科学结论 从基因表达数据中提取。 这项调查将考虑以下指标和分布特性 表情测量。将考虑适当的数据转换， 以及缺失和低于阈值的测量的归责方法。 用于处理测量过程中的背景噪声的声音技术将 被开发出来。我们将开发管理海量基因表达的方法 数据集。我们将针对这些数据定制并应用合理的数据挖掘方法 用于发现潜在科学的特征和关系 价值。我们将使用各种数据挖掘方法，如分类、 要研究的回归、相关性建模、聚类和图形技术 基因表达数据。我们将开发和应用合理的统计推断 方法对基因表达数据进行分析。这一目标涉及确认性方面。 统计研究，而不是探索性的方面。 基因表达数据的分析还没有建立在坚实的基础上 中提取有效推理的统计基础 显式统计模型的上下文。到目前为止，使用的技术是 主要是探索性和描述性的。该项目将开展必要的 关于推理问题的研究。预计广义线性模型 将发挥重要作用。的关系、模式和特点 当适当的调整时，基因表达数据被更准确地揭示 用于协变量、标记和处理指标，广义线性 模型为表示这些调整提供了灵活的框架。 该研究计划将为基因表达数据开发这些模型， 考虑到它们的独特特点。还预计， 基因表达数据的完整推理结构将需要贝叶斯 接近。这一方法将在该项目中得到检验。

DESCRIPTION (provided by applicant): Gene expression data, generated by current microarray technology, are a potential source of profound knowledge and insight into the human biological condition. Microarray data are a mass collection of facts and figures that must be organized, summarized, modeled, analyzed and interpreted to yield useful conclusions. The goal of this proposed research program is to develop statistical thinking and methods that will allow useful scientific conclusions to be drawn from gene expression data. This investigation will consider the metric and distributional properties of expression measurements. Appropriate data transformations will be considered, as well as imputation methodology for missing and sub-threshold measurements. Sound techniques for handling background noise in the measurement process will be developed. We will develop methods for managing massive genetic expression data sets. We will tailor and apply sound data mining methods to these data for the discovery of characteristics and relationships of potential scientific value. We shall use various data mining methods such as classification, regression, dependency modeling, clustering and graphical techniques to study gene expression data. We will develop and apply sound statistical inference methods to gene expression data. This aim deals with the confirmatory aspects of the statistical research, as opposed to the exploratory aspects. The analysis of gene expression data has not yet been put on a solid statistical footing with respect to extracting valid inferences within the context of an explicit statistical model. Techniques used to date have been mainly exploratory and descriptive. This project will carry out the necessary research on inference issues. It is anticipated that generalized linear models will play an important role. Relationships, patterns and characteristics of gene expression data are revealed more precisely when appropriate adjustments are made for covariates, markers and treatment indicators, generalized linear models provide a flexible framework for representing these adjustments. The research program will develop these models for gene expression data, taking their unique characteristics into account. It is also anticipated that a full inferential structure for gene expression data will require a Bayesian approach. This approach will be examined in the project.