Supporting Biomedical Discovery with the ROBOKOP Graph Knowledgebase.

使用 ROBOKOP 图知识库支持生物医学发现。

• 批准号: 10494653
• 负责人: Christopher Bizon
• 金额: $ 81.9万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-05 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10494653
• 关键词: Academia; Address; Age; Algorithmic Software; Algorithms; Area; Asthma; Biochemical; Biomedical Research; Case Study; Chemical Exposure; Clinical; Collection; Communities; Complex; Coronavirus Infections; Coupled; Data; Data Set; Data Store; Databases; Development; Disease; Drug Exposure; Ensure; FAIR principles; Generations; Genes; Goals; Graph; Growth; Immune; Individual; Industry; Informatics; Ingestion; International; Knowledge; Knowledge Discovery; Link; Lung diseases; Maintenance; Mediating; Methodology; Mining; Outcome; Pathway interactions; Patients; Pharmaceutical Preparations; Primary Ciliary Dyskinesias; Privatization; Public Domains; Publications; Research; Resource Development; Review Literature; Science; Scientific Advances and Accomplishments; Semantics; Services; Source; Structure; Support System; System; Technology; Testing; Translational Research; adverse outcome; asthma exacerbation; austin; base; community engagement; data infrastructure; design; drug induced liver injury; flexibility; hackathon; high throughput technology; improved; innovation; knowledge graph; knowledgebase; light weight; method development; network models; novel; pollutant; prototype; repository; research and development; success; tool; trend; user-friendly; virtual

The proliferation of high-throughput technologies has led to previously unimaginable growth in biomedical research data sets and knowledgebases. Nearly all these data and knowledge sources address specialized areas of biomedical research, leading to natural diversity but also growing disintegration between individual knowledgebases. This trend generates downstream inefficiencies when applying analytics to enable actionable knowledge discovery from databases. Growing efforts, both in academia and industry, are focused on the development of methods and tools to enable semantic integration and concurrent exploration of disparate biomedical knowledge sources. Recent innovations include the development of biomedical `knowledge graphs' (KGs) that support knowledge discovery through the application of querying and reasoning algorithms and tools. Our team has contributed to these efforts by developing a KG-based question-answering system termed Reasoning Over Biomedical Objects linked in Knowledge-Oriented Pathways (ROBOKOP). Herein, we propose synergistic research and development efforts that aim to significantly advance the ROBOKOP graph knowledgebase capabilities to contribute to high-impact applications across diverse biomedical research domains. Our overarching goal is to equip users with a unique and comprehensive knowledgebase system that supports the rapid generation of mechanistic hypotheses that can explain, validate, or predict biomedical phenomena. We will achieve our objectives by executing studies planned under the following Specific Aims: Aim 1. Enrich and Enhance the ROBOKOP graph knowledgebase. We will enhance the data and infrastructure of the ROBOKOP KB. Aim 2. Provide tools to explore the ROBOKOP graph knowledgebase. We will enhance the ROBOKOP KG by developing and employing novel reasoning tools for KG mining and edge inference. Aim 3. Prove utility and promote use of the ROBOKOP graph knowledgebase through impactful use cases. We will conduct several collaborative proof-of-concept research applications in diverse biomedical domains and diseases. We will actively promote community engagement, user acceptance, and broader impact of ROBOKOP. We expect that our diverse, cutting-edge approach to research, development, and community engagement, coupled with our high-impact biomedical applications, will lead to the formation of a core group of regular users, promote long-term sustainability, and generate impactful new scientific knowledge and mechanistic hypotheses for subsequent testing.

高通量技术的扩散导致了生物医学领域以前难以想象的增长， 研究数据集和知识库。几乎所有这些数据和知识来源都涉及专门的 生物医学研究领域，导致自然多样性，但也越来越多的个人之间的解体 知识库这种趋势在应用分析以实现可操作的 从数据库中发现知识。学术界和工业界越来越多的努力都集中在 开发方法和工具，使语义集成和并行探索不同的 生物医学知识来源。最近的创新包括生物医学“知识图”的发展 (KGs)通过应用查询和推理算法和工具来支持知识发现。 我们的团队通过开发一个基于KG的问答系统， 在面向知识的途径（ROBOKOP）链接的生物医学对象的推理。在此，我们建议 协同研究和开发工作，旨在显着推进ROBOKOP图 知识库的能力，以促进高影响力的应用程序在不同的生物医学研究 域.我们的首要目标是为用户提供一个独特而全面的知识库系统， 支持快速生成可以解释，验证或预测生物医学的机制假设 现象。我们将通过执行根据以下具体目标规划的研究来实现我们的目标： 1.丰富和增强ROBOKOP图形知识库。我们将加强数据和基础设施 关于ROBOKOP KB目标2.提供工具来探索ROBOKOP图形知识库。我们将 通过开发和采用新颖的KG挖掘和边缘推理工具来增强ROBOKOP KG 推论目标3.证明实用性，并通过有影响力的 用例我们将在不同的生物医学领域开展多项合作性概念验证研究应用。 领域和疾病。我们将积极促进社区参与，用户接受，以及更广泛的 机器人的影响。我们希望我们多样化的尖端研究、开发和 社区参与，加上我们的高影响力的生物医学应用，将导致形成一个 核心用户群，促进长期可持续性，并产生有影响力的新科学知识 和后续测试的机械假设。