Statistical and Computational Studies of Microarray Data

描述（由申请人提供）：微阵列已成为生物学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学过程的全局视图。然而，虽然它们的使用越来越广泛，但许多重要问题仍未得到解决，它们揭示重要见解的潜力尚未充分实现。这项工作的最初部分将是对微阵列表达值进行更准确的估计。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变化很大，这将被纳入新的算法中。这项工作的主要部分将是开发新技术，以发现和理解基因之间以及基因和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探索新的指标。来自多元统计学的密度估计技术和其他复杂的计算工具将被用来筛选数十亿种可能的组合排列。如果可能，将对发现具有显著性的组合进行深入检查和生物学验证。最后，一个统计框架将在广义线性模型设置，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的高度共线基因，将开发基于偏最小二乘的新计算技术。发现基因和删失患者生存时间之间的相关性的初步结果是有希望的，类似的方法将被开发和应用于在各种类型的表型数据的背景下识别预测基因。 候选人接受了应用和计算数学的培训，他现在的目标是将他的技能应用于生物信息学和功能基因组学的问题。拟议中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的全面培训。通过这个过渡期，候选人希望成为一名独立的研究人员，能够领导一个多学科团队，以综合的方法研究复杂的生物系统。

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.