III: Small: Techniques for Integrated Analysis of Graphs with Applications to Cheminformatics and Bioinformatics

III：小：图集成分析技术及其在化学信息学和生物信息学中的应用

• 批准号: 0917149
• 负责人: Ambuj Singh
• 金额: $ 48.83万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-15 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0917149&HistoricalAwards=false
• 关键词: III; Small; Techniques; Integrated; Analysis

A number of scientific endeavors generate data that can be modeled as graphs: high-throughput biological experiments on protein interactions, high throughput screening of chemical compounds, social networks, ecological networks and food-webs, database schemas and ontologies. Access and analysis of the resulting annotated and probabilistic graphs are crucial for advancing the state of scientific research, accurate modeling and analysis of existing systems, and engineering of new systems. This project aims to develop a set of scalable querying and mining tools for graph databases by integrating techniques from databases and data mining. The proposed research work is theoretical as well as empirical. New theoretical ideas and algorithms are being developed and these are being applied to the domains of Cheminformatics and Bioinformatics.The first research thrust examines primitives for graph data management and graph mining. A declarative query language for graphs is being investigated. This language is based on a formal language for graphs and a graph algebra, and separates the concerns of specification and implementation. Scalability of techniques for similarity search on graphs and mining for significant patterns is being investigated as a part of this thrust.The second research thrust applies the developed techniques to the domain of Cheminformatics. Specific tasks that are being examined are search for similar compounds, mining for significant motifs, diversity analysis, and analysis of macromolecular complexes.The final research thrust applies the developed methods to the domain of Bioinformatics. There has been an explosion of data of widely diverse biological data types, arising from genome-wide characterization of transcriptional profiles, protein-protein interactions, genomic structure, genetic phenotype, gene interactions, gene expression, proteomics, and other techniques. Techniques being developed can integrate and analyze data from multiple sources and models efficiently, while accelerating (interaction and function) prediction, and pathway discovery.Further information about the project can be found at the project web page http://www.cs.ucsb.edu/~dbl/0917149.php.

许多科学努力生成的数据可以建模为图：有关蛋白质相互作用，化学化合物，社交网络，生态网络和食品网络，数据库方案和本体学的高通量生物学实验。 对所产生的注释和概率图的访问和分析对于推进科学研究状态，对现有系统的准确建模和分析以及新系统的工程至关重要。 该项目旨在通过集成数据库和数据挖掘技术来开发一组图形数据库的可扩展查询和采矿工具。 拟议的研究工作既是理论上又是经验。正在开发新的理论思想和算法，并将其应用于化学信息和生物信息学的领域。首次研究推力研究了图形数据管理和图形挖掘的原始方法。正在研究一种声明性的查询语言。该语言基于图形和图代数的形式语言，并将规范和实现的关注点分开。在图形上进行相似性搜索技术的可伸缩性正在研究作为此推力的一部分。正在研究的特定任务是搜索类似的化合物，对大型基序的开采，多样性分析和大分子复合物的分析。最终的研究推力将开发的方法应用于生物信息学的领域。源自转录谱，蛋白质 - 蛋白质相互作用，基因组结构，遗传表型，基因相互作用，基因表达，蛋白质组学和其他技术的转录谱，蛋白质蛋白相互作用，基因组结构，蛋白质组学和其他技术的数据，爆炸爆炸了。正在开发的技术可以有效地集成和分析来自多个来源和模型的数据，同时加速（交互和功能）预测，并且可以在项目网页http://wwwww.cs.ucsb.edu/c.ucsb.edu/~dbl/c.dbl/0917149.php上找到有关该项目的信息。