EAGER: Cluster Detection in Graphs for Noisy, Incomplete Biological Data

EAGER：图表中噪声、不完整生物数据的聚类检测

• 批准号: 1242451
• 负责人: Susan Epstein
• 金额: $ 5万
• 依托单位: CUNY Hunter College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-15 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1242451&HistoricalAwards=false
• 关键词: EAGER; Cluster; Detection; Graphs; Noisy

The integration of gene annotations and omics (e.g., genomics, proteomics, metabolomics) data can provide important insights into noisy and incomplete biological data. Such data is often also on a scale presents computational challenges to many traditional algorithms. This project exploits Foretell, a local search algorithm originally created to accelerate solvers on large constraint satisfaction problems. Here Foretell is used to detect complex relationships among genes in context-specific protein-protein interaction (PPI) networks, with guidance from human experts. This is a novel, and potentially transformative, approach to provide new insights into the molecular and cellular mechanisms of fundamental biological processes. This flexible, innovative project is ideal for noisy, incomplete genomic data. It uses repeated local search guided by empirical biological knowledge to explore large weighted graphs under human direction. It provides users with meaningful feedback to reformulate their search for complex relationships among genes in context-specific PPI networks, and to devise new weight schemes to find them. Expected outcomes include a knowledge base of recurring clusters in Saccharomyces cerevisiae and the weight schemes used to detect them, a more flexible algorithm that detects and tabulates cluster features and provides meaningful feedback to the user, and a tool whose output suggests additional biological experiments. This project addresses, both in its design and its implementation, important questions in the discovery and application of computational approaches to biological networks.Knowledge derived from this project will be broadly applicable and well promulgated through publication and through a web site. The resultant knowledge base will support other researchers? detection of combinations of interacting genes and the interpretation of their results. While it advances discovery and understanding, this project will support interdisciplinary collaboration, disseminate its results broadly, and promote research by students in a predominantly female, minority-serving institution.

基因注释和组学（如基因组学、蛋白质组学、代谢组学）数据的整合可以为嘈杂和不完整的生物学数据提供重要的见解。这样的数据通常也在一定规模上对许多传统算法提出了计算挑战。该项目利用了Foretell，这是一种局部搜索算法，最初是为了加速求解大型约束满足问题而创建的。在这里，Foretell在人类专家的指导下，用于检测上下文特异性蛋白质蛋白相互作用（PPI）网络中基因之间的复杂关系。这是一种新颖的，具有潜在变革性的方法，为基本生物过程的分子和细胞机制提供了新的见解。这个灵活、创新的项目是嘈杂、不完整的基因组数据的理想选择。它利用经验生物学知识指导下的重复局部搜索来探索人类指导下的大型加权图。它为用户提供了有意义的反馈，以重新制定他们在特定环境的PPI网络中寻找基因之间复杂关系的搜索，并设计新的权重方案来找到它们。预期的结果包括在酿酒酵母中反复出现的集群的知识库和用于检测它们的权重方案，一个更灵活的算法，可以检测和制表集群特征并向用户提供有意义的反馈，以及一个输出建议额外生物实验的工具。本项目在设计和实施中都涉及到生物网络计算方法的发现和应用中的重要问题。从这个项目中获得的知识将广泛适用，并通过出版物和网站得到很好的宣传。由此产生的知识库将支持其他研究人员吗？相互作用基因组合的检测及其结果的解释。在促进发现和理解的同时，该项目将支持跨学科合作，广泛传播其成果，并促进这所以女性为主、服务于少数族裔的机构的学生的研究。