Supporting Biomedical Discovery with the ROBOKOP Graph Knowledgebase.

使用 ROBOKOP 图知识库支持生物医学发现。

基本信息

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
项目类别：
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

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

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项目摘要

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.

高通量技术的扩散已导致以前无法想象的生物医学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解决了专业生物医学研究的领域，导致自然多样性，但在个人之间的瓦解也不断增加知识库。这种趋势在应用分析时会产生下游效率低下从数据库中发现知识。在学术界和行业中，越来越多的努力都集中在开发方法和工具，以实现语义整合和同时探索不同的方法生物医学知识来源。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以KG为基础的提问系统为这些努力做出了贡献在知识途径（Robokop）中链接的生物医学对象的推理。在这里，我们建议旨在大大推动Robokop图的协同研究和开发工作知识库的能力有助于促进多种生物医学研究的高影响应用域。我们的总体目标是为用户提供独特而全面的知识库系统支持可以解释，验证或预测生物医学的机械假设的快速产生现象。我们将通过执行根据以下特定目的进行计划的研究来实现我们的目标：目标 1。富集和增强robokop图形知识库。我们将增强数据和基础架构 Robokop KB。 AIM 2。提供探索Robokop Graph知识库的工具。我们将通过开发和采用新颖的推理工具来开采KG和Edge来增强Robokop KG 推理。目标3。通过有影响力的用例。我们将在多种生物医学中进行几项协作概念证明研究应用领域和疾病。我们将积极促进社区参与，用户接受和更广泛的 Robokop的影响。我们期望我们多样化，尖端的研究，发展和社区参与，再加上我们高影响的生物医学应用，将导致形成普通用户的核心小组，促进长期可持续性并产生有影响力的新科学知识和机械假设，用于随后的测试。