MATHEMATICAL TOOLS FOR GENE EXPRESSION DATA ANALYSIS

描述(申请人摘要)： DNA微阵列技术和人类基因组测序已经取得了进展 到了现在可以监测基因表达水平的程度 基因组规模。这些数据是从根本上理解 分子水平上的生物过程，这将彻底改变医学 疾病和癌症的诊断和治疗，以及有效的设计 毒品。 尽管基因表达技术取得了进步，但它承诺的好处是 分子遗传学和医学的未来不会在没有 用于数据分析和信息提取的适当工具。没有经济能力 检测基因表达数据中对应的宏观模式 对于特定的细胞过程或表型特征，这些数据是， 并将继续，令人费解。 到目前为止，对这些新数据的分析工作还很少， 需要开发新的分析和计算工具或 采用其他科学学科的现有工具。这些工具 应该独立于任何描述基因表达的模型， 因为没有这样的预测模型可用。它们应该适合制作 大量数据的使用，同时减少了 数据的复杂性，使其易于理解。 在这个项目中，这些急需的全基因组基因表达数据工具 将进行分析。分析和计算方法，包括 已经成功地描述了物理世界，从量子力学到 图像处理，将会用到。将这些工具应用于基因表达数据 在教授的实验室中获得。波特斯坦和布朗，图案符合 具体的分子机制和表型特征将被确定。 该项目的目标是创建预测模型，用于诊断和 疾病和癌症的治疗。这些模型最终可能有助于阐明 分子水平上的生物过程的第一原理。

DESCRIPTION (Applicant's Abstract): DNA micro-array technology and the sequencing of the human genome have advanced to the point that it is now possible to monitor gene expression levels on a genomic scale. These data hold the key to fundamental understanding of biological processes on the molecular level, which will revolutionize medical diagnosis and treatment of diseases and cancer, and the design of effective drugs. Despite the progress in gene expression technology, its promised benefits for the future of molecular genetics and medicine will not materialize without appropriate tools for data analysis and information extraction. Without means of detecting macroscopic patterns in the gene expression data that correspond to specific cellular processes or phenotype characteristics, these data are, and will remain, incomprehensible. Little has been done so far toward the analysis of these new data, which requires the development of new analytical and computational tools or the adaptation of existing tools from other scientific disciplines. These tools should be independent of any model for the description of gene expression, since no such predictive model is available. They should be suitable for making use of the large quantities of data and at the same time reducing the complexity of the data to make them comprehensible. In this project, these much-needed tools for genome-wide gene expression data analysis will be developed. Analytical and computational methods which are already successful in describing the physical world, from quantum mechanics to image processing, will be used. Applying these tools to gene expression data obtained in the labs of Profs. Botstein and Brown, patterns that correspond to specific molecular mechanisms and phenotype characteristics will be identified. The goal of this project is to create predictive models for diagnosis and treatment of diseases and cancer. These models may eventually help elucidate the first principles of biological processes on the molecular level.