Analysis of Microarray Gene Expression of Tumor

描述（由申请人提供）： 由当前微阵列技术生成的基因表达数据是一种 人类生物学的深刻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据的集合， 必须进行组织、总结、建模、分析和解释， 有用的结论。这项研究计划的目标是开发 能够得出有用的科学结论的统计思想和方法 to be drawn拉from gene基因expression表达data数据. 本研究将考虑的度量和分布性质， 表达测量。将考虑适当的数据转换， 以及缺失和亚阈值测量的插补方法。 在测量过程中处理背景噪声的声音技术将 发展。我们将开发管理大量基因表达的方法 数据集。我们将对这些数据量身定制并应用合理的数据挖掘方法 发现潜在的科学特性和关系 值我们将使用各种数据挖掘方法，如分类， 回归，依赖模型，聚类和图形技术来研究 基因表达数据。我们将发展和应用健全的统计推断 基因表达数据。这一目标涉及确认方面 与探索性研究相反， 对基因表达数据的分析还没有放在坚实的基础上 统计立足于提取有效的推论内 一个明确的统计模型。迄今为止使用的技术 主要是探索性和描述性。该项目将开展必要的 研究推理问题。预计广义线性模型 将发挥重要作用。的关系、模式和特点 基因表达数据在适当的调整后可以更精确地显示出来 为协变量、标志物和治疗指标、广义线性 模型为表示这些调整提供了灵活的框架。 该研究项目将开发这些基因表达数据模型， 考虑到他们的独特性。我们还估计到 基因表达数据的完整推理结构将需要贝叶斯 approach.这一办法将在项目中加以审查。

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.