STTR Phase I: A platform for biological information integration and knowledge discovery

STTR第一期：生物信息整合和知识发现平台

• 批准号: 1416949
• 负责人: Jinfeng Zhang
• 金额: $ 22.5万
• 依托单位: INSILICOM LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-01 至 2015-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1416949&HistoricalAwards=false
• 关键词: STTR; Phase; platform; biological; information

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project is to help researchers to identify novel scientific hypotheses and make discoveries by harnessing the power of big data being generated in biological and biomedical fields. It is well known that big data have significant commercial potential, but also present some new challenges. For example, unstructured information in scientific publications needs to be converted to a structured form. This task is important for organizing existing scientific discoveries in a structured format for downstream applications. In addition, heterogeneous, large volume biological information needs to be integrated into a data structure effective for knowledge discovery. For example, integrating disease-protein relationships and protein-small molecule relationships will likely help us to identify small molecule drugs for treating diseases. Integrating the research findings from different sources is essential for understanding the big picture of biological systems. An effective integration will open the doors to important applications with commercial potential, such as drug repurposing and drug side effect prediction.This project aims to develop three commercial products. The first product is a software tool for bio-entity relationship extraction based on a novel, proprietary method for extracting bio-entity relationships from unstructured texts. The second product is a commercial database for context-specific molecular interaction information based on the integrated molecular interaction database (IMID) developed previously. IMID integrates multiple types of bio-entity relationships with versatile query functions. A commercial version of IMID will be built using updated information with additional functionalities. The third product is a platform for biological information integration and data mining with applications in drug repurposing and drug side effect prediction. Integrated bio-entity network (IBN) is a novel data structure for biological information integration and data mining. It offers some unique advantages compared to existing systems/approaches. Knowledge discovery tools based on IBN will facilitate biologists to search direct and indirect bio-entity relationships effectively. Two drug related data mining applications will be built: Drug repurposing search and drug side effect prediction, which utilize the unique advantage of IBN in identification of dysregulated pathways and subnetworks between two biological conditions.

这个小企业技术转让（STTR）第一阶段项目的更广泛的影响/商业潜力是帮助研究人员确定新的科学假设，并通过利用生物和生物医学领域产生的大数据的力量进行发现。众所周知，大数据具有巨大的商业潜力，但也提出了一些新的挑战。例如，科学出版物中的非结构化信息需要转换为结构化形式。这项任务对于以结构化格式组织现有的科学发现以供下游应用非常重要。 此外，异构的，大量的生物信息需要集成到一个数据结构有效的知识发现。 例如，整合疾病-蛋白质关系和蛋白质-小分子关系可能有助于我们识别治疗疾病的小分子药物。 整合不同来源的研究结果对于理解生物系统的全貌至关重要。 有效的整合将为具有商业潜力的重要应用打开大门，例如药物再利用和药物副作用预测。本项目旨在开发三种商业产品。第一个产品是一个软件工具，用于生物实体关系提取的基础上，一个新的，专有的方法，从非结构化文本提取生物实体的关系。 第二个产品是一个商业数据库的上下文特定的分子相互作用信息的基础上，集成的分子相互作用数据库（IMID）以前开发的。 IMID集成了多种类型的生物实体关系和通用的查询功能。 将利用最新信息建立IMID的商业版本，并增加功能。 第三个产品是生物信息集成和数据挖掘平台，应用于药物再利用和药物副作用预测。 集成生物实体网络（IBN）是一种用于生物信息集成和数据挖掘的新型数据结构。 与现有的系统/方法相比，它提供了一些独特的优势。 基于IBN的知识发现工具将有助于生物学家有效地搜索直接和间接的生物实体关系。将建立两个与药物相关的数据挖掘应用程序：药物再利用搜索和药物副作用预测，它们利用IBN在识别两种生物条件之间的失调途径和子网络方面的独特优势。