EAGER: Graph-Based Theoretical Models and Mining Algorithms for Bioinformatics Data Analysis

EAGER：用于生物信息学数据分析的基于图的理论模型和挖掘算法

• 批准号: 1049864
• 负责人: Xiaohua Hu
• 金额: $ 15万
• 依托单位: Drexel University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1049864&HistoricalAwards=false
• 关键词: EAGER; Graph; Based; Theoretical; Models

EAGER: Graph-based Theoretical Models and Mining Algorithms for Bioinformatics Data Analysis Project SummaryGraphs show up in a surprisingly diverse set of disciplines, ranging from computer networks to sociology, biology, ecology and many more. In recent years, huge amounts of data are generated and ultimately represented in graph format in the bioinformatics applications. Some are stored as a single large graph such as a protein-protein interaction network, while others are stored as a set of graph objects in a graph database such as chemical compounds in drug design and protein 2D/3D structures in bioinformatics. The main goal of this proposal is to develop novel graph-based theoretical models and algorithms to analyze bioinformatics data sets represented as either a single large graph or a graph database, focusing on the (1) development of efficient structure pattern discovery methods in graph databases based on graph decomposition and compression, and (2) development of mining algorithms for large scale-free network graphs, focusing on developing mixture model and statistical inference of hierarchical structure and missing link prediction in complex network. These algorithms will be validated by analyzing data sets arising in bioinformatics research. The proposed research will advance the understanding of bioinformatics and data mining algorithm research and development in graph data sets. It will also expand the application scope and push new frontiers for graph theory, bioinformatics and data mining. This research work is considered ?high risk high payoff? because it needs to develop novel theoretical graph-model and algorithms, radically different from the dominant random graph theory models and algorithmsIntellectual merit: The techniques and methods to be developed in this proposal build on state-of-the-art methods in bioinformatics, graph theory, data mining and database management. The research will result in improved understanding of the issues involved in designing efficient graph-based models, algorithms and methods in scientific data sets. The proposed project will design and develop a wide-range of novel data analysis algorithms and methods including structure pattern matching and discovery, and mining large scale-free network graphs. Broader impacts: The proposal will address a broad range of problems in the data analysis of scientific application domains such as bioinformatics, drug design, molecular biology, etc. Both the graph-based models and algorithms developed from this research are central to the computer science, and are generalizable and will be made publicly available for use in other domains, including personal or social contacts in sociology and epidemiology, author-co-citations in information science, the Internet and the World Wide Web in computer science and information technology. Research outcomes from this proposal can lead to more efficient and effective modeling and simulation mechanism of the biological network and advanced mathematical capabilities that are applicable through other science and engineering domain. The PI and Co-PI have a strong commitment to the integration of research and education, promotion of diversity, strong industrial partnership and broad dissemination of research results. The proposed research area lends itself to raising the scientific curiosity of students at many levels. Students will obtain significant exposure to the latest research. Both the research and education plans of the proposal are highly interdisciplinary, engaging students and faculty from various research areas and drawing from work on numerous fields of study. In particular, the plans serve to highlight the benefits of synthesizing bioinformatics, graph theory and data mining in creating the next generation of graph-based models, algorithms and tools for bioinformatics research. Armed with these models, algorithms and tools, bioinformatics researchers will discover more meaningful and pertinent knowledge/patterns and enable them to have a better understanding and interpretation of the data sets collected in their investigation. Students will gain an appreciation for the ability to understand, analyze and mine the growing range of bioinformatics data sets.

EAGER：基于图的生物信息学数据分析理论模型与挖掘算法 项目摘要图表出现在一系列令人惊讶的学科中，从计算机网络到社会学，生物学，生态学等等。近年来，在生物信息学应用中产生了大量的数据，并最终以图形格式表示。有些存储为单个大图，例如蛋白质-蛋白质相互作用网络，而另一些则存储为图数据库中的一组图对象，例如药物设计中的化合物和生物信息学中的蛋白质2D/3D结构。该提案的主要目标是开发新的基于图的理论模型和算法，以分析表示为单个大型图或图数据库的生物信息学数据集，重点是（1）开发基于图分解和压缩的图数据库中的有效结构模式发现方法，以及（2）开发大型无标度网络图的挖掘算法，重点研究了复杂网络的混合模型、层次结构的统计推断和缺失链预测。这些算法将通过分析生物信息学研究中产生的数据集进行验证。该研究将促进生物信息学的理解和数据挖掘算法的研究与开发。它还将扩大应用范围，推动图论，生物信息学和数据挖掘的新前沿。这项研究工作被认为是？高风险高回报？因为它需要开发新的理论图模型和算法，从根本上不同于占主导地位的随机图论模型和算法。智力优点：本提案中要开发的技术和方法建立在生物信息学、图论、数据挖掘和数据库管理中的最先进方法之上。这项研究将导致更好地理解设计有效的基于图形的模型，算法和方法在科学数据集所涉及的问题。该项目将设计和开发一系列新颖的数据分析算法和方法，包括结构模式匹配和发现，以及挖掘大型无标度网络图。更广泛的影响：该提案将解决科学应用领域（如生物信息学，药物设计，分子生物学等）数据分析中的广泛问题。这项研究开发的基于图形的模型和算法对计算机科学至关重要，并且是可推广的，将公开用于其他领域，包括社会学和流行病学中的个人或社会联系，作者共同引文信息科学，互联网和万维网在计算机科学和信息技术。从这个建议的研究成果可以导致更高效和有效的建模和仿真机制的生物网络和先进的数学能力，适用于通过其他科学和工程领域。PI和Co-PI坚定地致力于研究和教育的整合，促进多样性，强大的工业伙伴关系和广泛传播研究成果。拟议的研究领域有助于提高学生在多个层面的科学好奇心。学生将获得显着接触到最新的研究。该提案的研究和教育计划都是高度跨学科的，吸引了来自各个研究领域的学生和教师，并借鉴了许多研究领域的工作。特别是，这些计划突出了综合生物信息学、图论和数据挖掘在为生物信息学研究创建下一代基于图的模型、算法和工具方面的好处。 有了这些模型，算法和工具，生物信息学研究人员将发现更有意义和相关的知识/模式，并使他们能够更好地理解和解释在调查中收集的数据集。学生将获得理解，分析和挖掘不断增长的生物信息学数据集的能力。