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

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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.