Collaborative Research: Endowing Biological Databases with Analytical Power: Indexing, Querying, and Mining of Complex Biological Structures

南加州大学获得了一项资助，用于深入研究和开发新的、强大的、可扩展的索引、查询处理和数据挖掘方法，以构建可扩展的、高效的、基于分析的异构生物数据库系统。该项目是伊利诺伊大学计算机科学系和南加州大学分子与计算生物系的共同努力。它将集中于一套典型的基因组和生物学数据库，并开展以下主要研究主题：(1)开发高效、可扩展的复杂生物结构的索引和访问方法，重点是在大型多图中挖掘结构模式、密集循环图/网络挖掘和生物结构的相似性搜索；(2)开发跨异构、多关系生物数据库的高效挖掘机制。该项目将为复杂生物结构的索引和获取以及跨关系和跨数据库分析奠定坚实的基础。这样的数据库系统将允许访问大量的档案信息，以支持功能基因组学、蛋白质组学、环境研究以及其他生物和生物医学研究和应用中的生物数据分析。本提案中开发的软件系统将免费提供给学术界。此外，本项目还将开发生物网络和蛋白质结构两个网络数据库，以供生物界有效地检索和分析复杂的生物信息。从这个项目中获得的新知识将被整合到计算生物学、生物信息学、数据库系统和数据挖掘的课程和教科书中，以加强我们的教育和培训计划。参与这些项目的人员，包括女性和少数民族研究人员，将受到系统的培训，获得的新知识将用于信息分析人员、生物信息学家、计算生物学家和大学生的教育和培训。

The University of Southern California is awarded a grant to perform in-depth research and development of new, powerful, and scalable indexing, query processing, and data mining methods for construction of scalable, efficient, and analysis-based, heterogeneous biological database systems. The project is a joint effort between the Department of Computer Science in the University of Illinois and the Department of Molecular and Computational Biology in the University of Southern California. It will focus on a set of typical genomic and biological databases and work on the following major research themes: (1) development of efficient and scalable methods for indexing and accessing of complex biological structures, with the emphasis on mining structural patterns in large multi-graphs, mining dense recurrent graphs/networks, and similarity search on biologic structures, and (2) development of efficient and effective mechanisms for mining across heterogeneous, multi-relational bio-databases. This project will set up a solid foundation for indexing and accessing of complex biological structures and for cross-relational and cross-database analysis. Such database systems will allow access to large amounts of archival information to support biological data analysis in functional genomics, proteomics, environmental studies, and other biological and biomedical research and applications. The software systems developed in this proposal will be made freely available to the academic community. In addition, the project will develop two web databases, one on biological networks and the other on protein structures, for the biological community to efficiently retrieve and analyze the complex biological information. The new knowledge obtained from this project will be integrated into courses and textbooks on computational biology, bioinformatics, database systems, and data mining to enhance our education and training programs. The personnel working on these projects, including female and minority researchers, will be trained systematically, and new knowledge obtained will be used in education and training of information analysts, bioinformaticians, computational biologists, and university students.