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.

许多科学努力产生的数据可以建模为图形：蛋白质相互作用的高通量生物实验，化合物的高通量筛选，社会网络，生态网络和食物网，数据库模式和本体论。访问和分析结果的注释和概率图对于推进科学研究的状态，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。本项目旨在通过整合数据库和数据挖掘技术，为图数据库开发一套可扩展的查询和挖掘工具。提出的研究工作是理论和实证。新的理论思想和算法正在发展，这些正在应用于化学信息学和生物信息学领域。第一个研究重点是研究图数据管理和图挖掘的基本要素。正在研究一种用于图的声明性查询语言。该语言基于图形和图形代数的形式语言，并将规范和实现的关注点分开。图上的相似性搜索和重要模式挖掘技术的可扩展性正在作为这一推动力的一部分进行研究。第二个研究重点是将已开发的技术应用于化学信息学领域。正在研究的具体任务是寻找相似的化合物，挖掘重要的基序，多样性分析和大分子复合物分析。最后的研究重点是将开发的方法应用于生物信息学领域。由于转录谱的全基因组特征、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，各种各样的生物数据类型的数据爆炸式增长。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（相互作用和功能）预测和路径发现。有关该项目的进一步信息可以在该项目的网页http://www.cs.ucsb.edu/~dbl/0917149.php上找到。