Exploring Massive Gene Expression Data With A Novel Statistical Notion-Liquid Association

用新的统计概念——液体关联探索海量基因表达数据

• 批准号: 0201005
• 负责人: Ker-Chau Li
• 金额: $ 76万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-07-01 至 2007-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0201005&HistoricalAwards=false
• 关键词: Exploring; Massive; Gene; Expression; Data

Underlying most methods for analyzing gene expression is the key notion of profile similarity. Genes with similar profiles are likely to form structural complex or to participate in common cellular processes. Yet whether two genes are co-expressed or not is often affected by activities of other genes and sometimes a positive correlation can be turned into a negative one. In this research, Liquid association (LA) is introduced as a method to quantify changes in correlation. A preliminary study showed that LA can help identify functionally related genes that have no profile similarity. This development is harbored on three fronts: biological motivation, large scale computation, and statistical theory. The specific aims of the LA system include (1) to identify a small list of master expression genes- genes that have more expressional influence than others; (2) to help understand how genes regulate each other through important metabolic intermediates; (3) to analyze the co-expression pattern for genes associated with a known pathway; (4) to help the functional prediction of unknown genes; and (5) to provide an expression network through master genes. Large scale gene expression profiling has become increasingly more popular in tumor research and many other biological areas. Microarrays enable the full genome mRNA measurement. They have been applied to monitor gene activities under various physiological or environmental conditions. Investigations on differential expression between normal and disease tissues or cell-lines have also led to the identification of genes with potential diagnostic and clinic values. The LA-system will help unearth the biological information hidden beneath many expression databases that are publicly accessible. The results will be available for free use by other academic researchers. This grant is made under the Joint DMS/NIGMS Initiative to Support Research Grants in the Area of Mathematical Biology. This is a joint competition sponsored by the Division of Mathematical Sciences (DMS) at the National Science Foundation and the National Institute of General Medical Sciences (NIGMS) at the National Institutes of Health.

大多数分析基因表达的方法的基础是图谱相似性的关键概念。具有相似谱的基因可能形成结构复合体或参与共同的细胞过程。然而，两个基因是否共表达往往受到其他基因活性的影响，有时正相关性可以变成负相关性。在这项研究中，液体协会（LA）被引入作为一种方法来量化相关性的变化。一项初步研究表明，LA可以帮助识别没有谱相似性的功能相关基因。这种发展在三个方面：生物学动机，大规模计算和统计理论。LA系统的具体目标包括：（1）鉴定一小部分表达主基因--比其他基因具有更大表达影响的基因;（2）帮助理解基因如何通过重要的代谢中间产物相互调节;（3）分析与已知途径相关的基因的共表达模式;（4）帮助未知基因的功能预测;（5）分析与已知途径相关的基因的共表达模式。和（5）通过主基因提供表达网络。 大规模基因表达谱分析在肿瘤研究和许多其他生物学领域变得越来越流行。微阵列使全基因组mRNA测量成为可能。它们已被应用于监测在各种生理或环境条件下的基因活性。对正常和疾病组织或细胞系之间差异表达的研究也导致了具有潜在诊断和临床价值的基因的鉴定。LA系统将有助于发掘隐藏在许多公开访问的表达数据库下的生物信息。研究结果将供其他学术研究人员免费使用。这项赠款是根据联合DMS/NIGMS倡议，以支持研究赠款在数学生物学领域。这是一项由美国国家科学基金会数学科学部（DMS）和美国国立卫生研究院国家普通医学科学研究所（NIGMS）赞助的联合竞赛。