Statistical and Computational Studies of Microarray Data

微阵列数据的统计和计算研究

• 批准号: 6748926
• 负责人: Peter J Park
• 金额: $ 14.16万
• 依托单位: BOSTON CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-06-01 至 2008-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6748926
• 关键词: data management; functional /structural genomics; gene expression; gene interaction; mathematical model; method development; microarray technology; phenotype; statistics /biometry; technology /technique development

DESCRIPTION (provided by applicant): Microarrays have emerged as a new tool in biological and clinical research, giving a global view of a biological process in an unprecedented scale by simultaneous measurements of expression levels for thousands of genes. However, while their use is becoming widespread, many important issues remain unresolved and their potential for revealing important insights has not been fully realized. The initial part of this work will be on a more accurate estimation of microarray expression values. For example, performance of different probes on oligonucleotide arrays appears to vary widely depending on the melting temperature of the probe sequence, and this will be incorporated in a new algorithm. The main part of the work will be on developing new techniques for the discovery and understanding of complex interactions among genes as well as between genes and phenotypes. Moving beyond pairwise linear correlations, nonlinear and higher-order interactions among multiple genes will be explored with novel metrics. Density estimation techniques from multivariate statistics and other sophisticated computational tools will be employed to sift through billions of possible combinatorial arrangements. Those combinations found to be significant will be examined in depth and biologically validated when possible. Finally, a statistical framework will be developed in the generalized linear model setting in order to understand the relationship between genotypic and phenotypic data. To handle the large number of highly collinear genes in expression data, new computational techniques based on partial least squares will be developed. Preliminary results in finding correlations between genes and censored patient survival times have been promising, and similar methods will be developed and applied to identify predictive genes in the context of various types of phenotypic data. The candidate has been trained in applied and computational mathematics, and he now aims to apply his skills to problems in bioinformatics and functional genomics. The proposed award will allow him to receive a thorough training in molecular biology and genomics at Harvard Medical School and Children's Hospital in Boston. Through this transitional period, the candidate would like to become an independent investigator, able to lead a multidisciplinary team in an integrated approach to studying complex biological systems.

描述（由申请人提供）：微阵列已成为生物和临床研究中的一种新工具，通过同时测量数千个基因的表达水平，以前所未有的规模提供生物过程的全局视图。然而，虽然它们的使用越来越广泛，但许多重要问题仍未解决，它们揭示重要见解的潜力尚未得到充分实现。这项工作的最初部分将是更准确地估计微阵列表达值。例如，寡核苷酸阵列上不同探针的性能似乎根据探针序列的解链温度而有很大差异，这将被纳入新算法中。这项工作的主要部分将是开发新技术来发现和理解基因之间以及基因与表型之间的复杂相互作用。超越成对线性相关性，多个基因之间的非线性和高阶相互作用将通过新的指标进行探索。来自多元统计的密度估计技术和其他复杂的计算工具将用于筛选数十亿种可能的组合排列。那些被发现具有重要意义的组合将被深入检查，并在可能的情况下进行生物学验证。最后，将在广义线性模型设置中开发统计框架，以了解基因型和表型数据之间的关系。为了处理表达数据中大量高度共线性的基因，将开发基于偏最小二乘的新计算技术。寻找基因与审查患者生存时间之间相关性的初步结果是有希望的，并且将开发和应用类似的方法来识别各种类型表型数据背景下的预测基因。 该候选人接受过应用和计算数学方面的培训，他现在的目标是将自己的技能应用于生物信息学和功能基因组学的问题。拟议的奖项将使他能够在哈佛医学院和波士顿儿童医院接受分子生物学和基因组学方面的全面培训。在这个过渡时期，候选人希望成为一名独立的研究者，能够领导一个多学科团队以综合方法研究复杂的生物系统。