Advanced Approaches for Integration and Analysis of Genomic Data

数据的过度可获得性和知识的可获得性不足是生物信息学在未来几年面临的一个严峻挑战。显然，有效的技术不仅用于存储和检索目的，而且还用于挖掘基因组数据以增加我们的知识。然而，基因组数据的高维和海量给数据集的分析和可视化带来了非常具有挑战性的问题。该项目研究分析基因表达数据并将其整合到生物学研究中的新方法。将开发可迭代和交互地用于挖掘数据的新算法和工具。这些策略包括用于集成异类数据的元数据层次结构、用于高维数据的基于集群的索引、用于分类的维间分析和用于模式分析的动态交互可视化。这些方法将由研究生物体的表型和基因迭代的生物学家进行实地测试。该项目将提供一个灵活的、可扩展的工作台环境，准备用于一般基因组数据分析。除了教育发展活动外，该项目的影响还将通过在处理大规模多维数据集的其他领域的广泛应用而得到加强。

The over-availability of data and the under-availability of knowledge present a critical challenge for biological informatics in the years to come. Clearly, effective techniques are need not only for storage and retrieval purposes, but also for mining genomic data to increase our knowledge. However, the high dimensionality and enormous size of genomic data pose very challenging problems in analysis and visualization of the data sets. This project investigates novel approaches to analyzing gene expression data and integrating them into biological research. New algorithms and tools that can be used iteratively and interactively to mine the data will be developed. The strategies include a meta data hierarchy for integration of heterogeneous data, cluster-based indexing for high-dimensional data, inter-dimensional analysis for classification, and dynamic interactive visualization for pattern analysis. The approaches will be field-tested by biologists investigating an organism's phenotype and genotype iterations. The project will deliver a flexible, scalable workbench environment ready to be used for general genomic data analysis. In addition to education development activities, the project's impact will be enhanced by broad applications in other fields that handle large-scale multi-dimensional data sets.