MATHEMATICAL TOOLS FOR GENE EXPRESSION DATA ANALYSIS

用于基因表达数据分析的数学工具

• 批准号: 6388293
• 负责人: Orly Alter
• 金额: $ 8.85万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-04-01 至 2005-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6388293
• 关键词: analytical method; data collection methodology /evaluation; diagnosis design /evaluation; gene expression; human tissue; information retrieval; mathematical model; model design /development; neoplasm /cancer diagnosis; neoplasm /cancer therapy

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.

描述（申请人的摘要）： DNA微阵列技术和人类基因组测序取得了进步 现在可以监测基因表达水平 基因组规模。这些数据掌握着基本理解的关键 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 药物。 尽管基因表达技术取得了进步，但它所承诺的好处 如果没有，分子遗传学和医学的未来就不会实现 用于数据分析和信息提取的适当工具。没有手段 检测对应的基因表达数据中的宏观模式 对于特定的细胞过程或表型特征，这些数据是， 并将继续难以理解。 到目前为止，对这些新数据的分析还很少， 需要开发新的分析和计算工具或 改编其他科学学科的现有工具。这些工具 应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 利用大量数据，同时减少 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。分析和计算方法是 已经成功地描述了物理世界，从量子力学到 图像处理，会用到。将这些工具应用于基因表达数据 在教授的实验室获得。 Botstein 和 Brown，对应的模式 将确定具体的分子机制和表型特征。 该项目的目标是创建用于诊断和预测的预测模型 疾病和癌症的治疗。这些模型最终可能有助于阐明 分子水平上生物过程的首要原理。