An Extensible Semantic Bridge between Biodiversity and Genomics

内布拉斯加州大学获得了一项资助，用于开发用于调查和发现的语义网络基础设施（SCID），这是一种基于web的通用本体软件基础设施，它(1)通过实现来自每个领域的异构数据库的无缝联合，将众多生物研究兴趣联合起来；(2)支持科学家高效和有效地使用本体来加强调查和发现；(3)通过通用用户界面简化生物信息学工具的使用，用于查询数据、增强数据分析和解释科学结果，从而使那些进入或交叉进入新研究领域的人能够发现。利用基因组学、博物馆科学和计算机科学的教员之间的长期合作，SCID团队将通过以下方式增强其现有的原型：(1)开发一个“种子”本体，将生物多样性和基因组学的多个选定数据库结合起来；(2)提供一个应用程序编程接口（API），允许新的和现有的生物信息学工具轻松“插入”到我们的系统中。(3)开发工具，以促进通过协作编辑技术在开放论坛上达成科学共识的过程；(4)开发工具，以支持本体论开发、不同数据存储的联合以及调查和发现活动中的决策制定。该项目将通过联合和统一生物多样性和基因组学的数据源，解锁异构的大型数据库，以及授权调查和发现，推进基因组学和生物多样性研究。它还将推进机器学习、多智能体系统和语义技术的计算领域，用于从大型数据存储库中提取信息，支持用户进行调查和发现，并促进基于社区的知识工程。作为SCID的一部分开发的新的计算模型将促进本体的构建，这些本体将不同的数据库联系在一起，支持自动推理和演绎，允许数据一致性检查，并允许提出复杂的查询，从而创建一个创新的、动态的科学结构。

The University of Nebraska is awarded a grant to develop the Semantic Cyberinfrastructure for Investigation and Discovery (SCID), a general-purpose Web-based ontological software infrastructure that (1) unites numerous biological research interests by enabling seamless federation of heterogeneous databases from each area; (2) supports scientists in the efficient and effective use of ontologies to enhance investigation anddiscovery; and (3) streamlines the use of bioinformatics tools via common user interfaces for querying data, enhanced data analyses, and interpretation of scientific results, thus enabling discovery by those who enter or cross over into new research fields. Leveraging its long-standing collaboration between faculty from genomics, museum science, and computer science, the SCID team will enhance its existing prototype by (1) developing a "seed" ontology that unites multiple selected databases in biodiversity and genomics, (2) providing an application programming interface (API) to allow new and existing bioinformatics tools to easily "plug in" to our system, (3) developing tools to facilitate the process of reaching scientific consensus in open forums through collaborative editing technologies, and (4) developing tools to support ontological development, federation of disparate data stores, and decision making during investigation and discovery activities. This project will advance genomics and biodiversity research by federating and unifying data sources in biodiversity and genomics, unlocking heterogeneous, largescale databases, and empowering investigation and discovery. It will also advance the computational areas of machine learning, multiagent systems, and semantic technologies for extracting information from large data repositories, supporting users in making investigations and discoveries, and facilitating community-based knowledge engineering.New computational models that are developed as part of SCID will facilitate the building of ontologies that tie together disparate databases, support automatic inferences and deductions, allow for data consistency checks, and allow posing of sophisticated queries, thus creating an innovative, dynamic scientific structure.