FUNCTIONAL CLUSTERING APPROACH TO GENOMIC ANALYSIS

描述：(申请人描述) 转录调控是细胞的主要决定因素之一。 表型。最近的全基因组表达研究表明，癌症 细胞显示转录本丰度的全局变化，i)确定 肿瘤行为和ii)预测临床过程和结果。在这里我们 描述第一种智能、可扩展且自动化的方法 确定这些数据更广泛的生物学意义。具体来说， 这些方法在计算上检测到成分的变化规律 大规模表达数据中的生物路径，使用知识库 关于基因功能的信息。在这笔拨款的第一阶段，我们会： 用发表的45,000多篇文章填充功能性遗传知识库 至少200个基因与两种公认的肿瘤相关的事实 子过程(细胞程序性死亡和有丝分裂细胞周期)。 开发两种算法来识别与功能相关的子集 这些基因来自标准的表达数据。 评估这些算法检测生物学意义的能力 I)我们所知的200个基因的完整集合 碱基和II)来自有限的一组差异调控基因 癌症相关的表达数据。 建议的商业应用：不可用

DESCRIPTION: (Applicant's Description) Regulation of mRNA transcription is one of the major determinants of cellular phenotype. Recent genome-wide expression studies establish that cancerous cells display global alterations in transcript abundance that i) determine neoplastic behavior and ii) predict clinical course and outcome. Here we describe the fIrst intelligent, scaleable, and automated approach to identifying the broader biological significance of these data. Specifically, these methods computationally detect the altered regulation of components of biological pathways in large-scale expression data, using a knowledge base of information about gene function. In Phase I of this grant, we will: Populate a functional genetic knowledge base with more than 45,000 published facts on at least 200 genes involved in two well-established neoplastic subprocesses (programmed cell death and the mitotic cell cycle). Develop two algorithms that will identify functionally related subsets of these genes from standard expression data. Evalute the ability of these algorithms to detect biologically meaningful clusters of genes within I) the complete set of 200 genes in our knowledge base and ii) differentially regulated genes from a limited set of cancer-related expression data. PROPOSED COMMERCIAL APPLICATION: NOT AVAILABLE