Visant-Predictome: A System for Integration, Mining Visualization and Analysis

Visant-Predictome：集成、采矿可视化和分析系统

• 批准号: 7287965
• 负责人: CHARLES DELISI
• 金额: $ 44.69万
• 依托单位: BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-07-01 至 2010-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7287965
• 关键词: Address; Archives; Automobile Driving; Bayesian Method; Binding; Binding Sites; Biological; Cell physiology; Cellular Structures; Chemicals; Communication; Communities; Complex; Computer Systems Development; Computer software; Condition; Data; Data Sources; Databases; Dependence; Dependency; Development; Dictionary; Disease; Educational workshop; Electronic Mail; Facility Construction Funding Category; Genbank; Genes; Goals; Imagery; Information Systems; Link; Machine Learning; Maintenance; Methods; Mining; Modeling; Names; Network-based; Numbers; Online Mendelian Inheritance In Man; Phylogenetic Analysis; Proteomics; RNA Splicing; Reaction; Reporting; Score; Software Tools; Source; Structure; System; Systems Integration; Techniques; Technology; Tertiary Protein Structure; Update; Ursidae Family; Variant; Visual; Weight; base; chemical reaction; data mining; improved; models and simulation; open source; outreach; protein protein interaction; software development; statistics; tool; tool development; web-accessible; wiki

DESCRIPTION (provided by applicant): Recent and continuing technological advances are producing large amounts of disparate data about cell structure, function and activity. This is driving the development of tools for storing, mining, analyzing, visualizing and integrating data. This proposal describes the VisANT system: a tool for visual data mining that operates on a local database which includes results from our lab, as well as automatically updated proteomics data from web accessible databases such as MIPS and BIND. In addition to accessing its own database, a name normalization table (i.e. a dictionary of identifiers), permits the system to seamlessly retrieve sequence, disease and other data from sources such as GenBank and OMIM. The visualization tool is able to reversibly group related sets of nodes, and display and duplicate their internal structure, providing an approach to hierarchical representation and modeling. We propose to build further on these unique features by including capabilities for mining and representing chemical reactions, orthologous networks, combinatorially regulated transcriptional networks, splice variants and functional hierarchies. Software is open source, and the system also allows users to exchange and integrate the networks that they discover with those of others.

描述（由申请人提供）：最新和持续的技术进步正在产生有关细胞结构，功能和活动的大量数据。这正在推动开发用于存储，采矿，分析，可视化和集成数据的工具。该提案描述了Visant System：一种在本地数据库上运行的视觉数据挖掘的工具，该工具包括我们实验室的结果，以及从Web访问数据库（例如MIPS）和BIND绑定的可访问数据库中自动更新的蛋白质组学数据。除了访问自己的数据库外，名称归一化表（即标识符词典）允许该系统从GenBank和Omim等来源中无缝检索序列，疾病和其他数据。可视化工具能够可逆地分组相关的节点集，并显示和复制其内部结构，从而提供了层次表示和建模的方法。我们建议通过包括采矿和代表化学反应，直系同源网络，组合调节的转录网络，剪接变体和功能层次结构的能力，以进一步建立这些独特的特征。软件是开源的，该系统还允许用户交换和集成与其他人发现的网络。