High Dimensional Methods for Complex Data Refining

复杂数据精炼的高维方法

• 批准号: 0406091
• 负责人: Ker-Chau Li
• 金额: $ 20.32万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-06-01 至 2008-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0406091&HistoricalAwards=false
• 关键词: Dimensional; Methods; Complex; Data; Refining

Regression analysis aims at the study of the relationship between input variables X and out variables Y. Difficulties occur when no parametric model is known, and yet the number of variables is large. Dimension reduction methods for overcoming such difficulties have been investigated by many authors. To embrace many aspects of dimension reduction under one common roof, a new forum called the Z-mediated approach is proposed. In this setting, in addition to the input and out variables, a third group of variables Z is introduced, which fills the role of mediating the change in the relationship between X and Y. Typically the number of Z variables is much larger than the number of X or Y variables. But only a small portion of Z variables may have a real influence. New methods will be constructed to reduce the dimension of X, Y and Z. A wave of cutting-edge statistical activities have arrived at a time when there is an explosive demand for processing large data sets in the life sciences, such as those from microarrays and medical imaging. The motivation of this proposal comes from a dynamic perspective about complex gene regulation where two functionally associated genes X and Y may be mediated by a third unknown gene Z. The challenge is how to identify a short list of candidate gene Z based on microarray data alone. The methodology developed here can be used for elucidating the interplay between disease, genes, and metabolic pathways, thus contributing to drug discovery and benefiting society. The results will be disseminated not only via standard publication, but also by constructing a website for public access. Interdisciplinary training of students to work in bioinformatics is also provided.

回归分析的目的是研究输入变量X和输出变量Y之间的关系。当参数模型未知，但变量数量较多时，就会出现困难。克服这些困难的降维方法已经被许多作者研究过。为了将降维的多个方面包含在一个共同的屋檐下，提出了一种新的论坛，称为Z-中介方法。在这种情况下，除了输入和输出变量外，还引入了第三组变量Z，它起到了调节X和Y之间关系变化的作用。通常情况下，Z变量的数量远远大于X或Y变量的数量。但Z变量中只有一小部分可能会产生真正的影响。将构建新的方法来降低X、Y和Z的维度。在生命科学对处理大数据集的爆炸性需求之际，一波尖端统计活动已经到来，例如来自微阵列和医学成像的数据。这一建议的动机来自于一种关于复杂基因调控的动态观点，其中两个功能相关的基因X和Y可能由第三个未知基因Z介导。挑战是如何仅基于微阵列数据来识别候选基因Z的简短列表。这里发展的方法学可以用来阐明疾病、基因和代谢途径之间的相互作用，从而有助于药物发现和造福社会。结果不仅将通过标准出版物传播，还将通过建立一个供公众访问的网站来传播。还提供了学生从事生物信息学工作的跨学科培训。