Analysis of Microarray Gene Expression of Tumor

肿瘤微阵列基因表达分析

基本信息

批准号：
6514889
负责人：
Mei-Ling Ting Lee
金额：
$ 8.48万
依托单位：
BRIGHAM AND WOMEN'S HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2001
资助国家：
美国
起止时间：
2001-07-13 至 2004-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/6514889
关键词：
computer program /software data management human data leiomyoma leukemia liposarcoma mathematical model microarray technology neoplasm /cancer classification /staging neoplasm /cancer genetics oncogenes uterus neoplasms

项目摘要

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.

描述（由申请人提供）：通过当前的微阵列技术生成的基因表达数据是对人类生物学的深刻知识和洞察力的潜在来源健康）状况。微阵列数据是事实和数据的大量集合，必须进行组织、总结、建模、分析和解释以产生有用的结论。该拟议研究计划的目标是开发统计思维和方法将得出有用的科学结论从基因表达数据中提取。这项研究将考虑以下指标的度量和分布特性表达测量。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得到开发。我们将开发管理大规模基因表达的方法数据集。我们将为这些数据量身定制并应用完善的数据挖掘方法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，例如分类、回归、依赖建模、聚类和图形技术的研究基因表达数据。我们将开发并应用可靠的统计推断基因表达数据的方法。该目标涉及确认方面统计研究，而不是探索性方面。基因表达数据的分析尚未有坚实的基础关于在范围内提取有效推论的统计基础显式统计模型的背景。迄今为止使用的技术是主要是探索性和描述性的。该项目将进行必要的推理问题的研究。预计广义线性模型将发挥重要作用。关系、模式和特征适当的调整可以更准确地揭示基因表达数据是为协变量、标记物和治疗指标、广义线性模型提供了一个灵活的框架来表示这些调整。该研究计划将为基因表达数据开发这些模型，考虑到他们的独特特征。还预计基因表达数据的完整推理结构将需要贝叶斯方法。该方法将在项目中进行检验。