Regenbase: A Searchable Database to Organize Regeneration Knowledge via Ontologie

描述(申请人提供)：本申请寻求资金用于开发RegenBase--一种新型信息系统，将神经科学家和细胞生物学家研究神经系统损伤、疾病和细胞运动的各种数据与其他资源无缝集成，如神经科学信息框架和生物测定本体。在过去的十年里，美国国立卫生研究院资助了信息学工具和本体论的开发，以允许对人类基因组计划产生的海量和多样化的数据集进行整合和询问。在神经科学领域，在创建和注释揭示基因表达和连接模式的大型解剖数据集方面取得了最大进展。Genesat和BrainMaps是很好的例子，使用神经科学信息框架(NIF)门户网站可以很容易地进行搜索。但搜索与修复受损的NRVOUS系统有关的信息仍然令人惊讶地困难。要克服这一障碍，关键是要构建允许语义的基本工具 Web方法将不同的数据存储库与允许它们被解释和分析的本体联系起来。这一举措的成功关键取决于有效的信息解决方案，以将神经系统损伤方面的神经科学家产生的各种(当前和未来)数据类型以及大规模筛选工作(如分子图书馆探测中心网络，MLPCN)整合到一致的数据集中，并使不同背景和不同目标的科学家能够访问、解释和操作这些数据集。我们建议开发一个基于知识的、可扩展的互联组件信息系统，该系统利用语义网技术和领域级本体。这个系统暂时被称为RegenBase(再生数据库)。在过去的十年中，语义网技术的发展取得了巨大的进步，其目标是将知识形式化，将不同领域的信息链接起来，并整合来自不同来源的大型异质数据集。为了在资源有限的情况下快速开发RegenBase，我们将利用国家生物声学中心和最近推出的生物分析本体的技术和工具。RegenBase系统的长期目标是通过语义“关联数据”方法实现无缝的“动态”数据集成和分析，这种方法可根据信息量和复杂性进行扩展。RegenBase将整合生物医学领域级本体，包括我们最近开发的生物分析本体(BAO)，以在语义上关联相关数据类型，并提供基本实验和筛选结果的知识上下文。这个拟议的RegenBase系统的首要目标是允许普通科学家将神经系统损伤和疾病研究的数据和结果与其他领域的数据和知识联系起来，重点放在分子靶标和干扰其功能的小分子上，以加快新疗法的开发。

DESCRIPTION (provided by applicant): This application seeks funds to develop RegenBase - a novel information system to seamlessly integrate diverse data that are produced by neuroscientists and cell biologists studying nervous system injury, disease and cell motility with other resources, such as the Neuroscience Information Framework and the BioAssay Ontology. Over the past decade the NIH has funded the development of informatics tools and ontologies to allow the integration and interrogation of the massive and diverse data sets that have been produced by the human genome project. In the area of neuroscience the most advancement have been made in the creation and annotation of large anatomical data sets that reveal patterns of gene expression and connectivity. Genesat and the BrainMaps are excellent examples and are easily searched using the Neuroscience Information Framework (NIF) portal. But it is still surprisingly difficult to search for information related to repairing the injured nrvous system. To overcome this road block it is critical to build the essential tools that allow semantic web approaches to link diverse data repositories with ontologies that allow them to be interpreted and analyzed. The success of this initiative critically relies on an effective informatcs solution to integrate the various (current and future) data types generated by neuroscientists working on nervous system injury, as well as large-scale screening efforts (such as the Molecular Libraries Probe Center Network, MLPCN) into coherent data sets and to make them accessible, interpretable, and actionable for scientists of different backgrounds and with different objectives. We propose to develop a novel knowledge-based, extensible information system of interconnected components that leverages semantic-web technologies and domain level ontologies. This system is tentatively called RegenBase (Regeneration dataBase). Tremendous progress has been made during the last decade developing semantic web technologies with the goals of formalizing knowledge, linking information across different domains, and integrating large heterogeneous data sets from diverse sources. To develop RegenBase on a fast-track with limited resources, we will leverage technologies and tools from the National Center for Biological Onotology and the recently launched BioAssay Ontology. The long-term goal of the RegenBase system is seamless "on-the fly" data integration and analysis via a semantic "Linked Data" approach that is scalable with respect to information volume and complexity. RegenBase will incorporate biomedical domain-level ontologies, including our recently developed BioAssay Ontology (BAO), to semantically associate related data types and to provide a knowledge context of the underlying experiments and screening outcomes. The overarching goal of this proposed RegenBase system is to allow bench scientists to link data and results from studies on nervous system injury and disease to data and knowledge from other domains with an emphasis on molecular targets and the small molecules that perturb their function to speed the development of novel therapeutics.