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: 10697371
• 负责人: Christopher Bizon
• 金额: $ 78.8万
• 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/10697371
• 关键词: Academia; Acceleration; Address; Age; Algorithms; Area; Asthma; Biochemical; Biomedical Research; Case Study; Chemical Exposure; Clinical; Collection; Communities; Complex; Coronavirus Infections; Coupled; Data; Data Set; Databases; Development; Disease; Disparate; Drug Exposure; Ensure; FAIR principles; Generations; Genes; Goals; Graph; Growth; Immune; Individual; Industry; Informatics; Infrastructure; Ingestion; International; Knowledge; Knowledge Discovery; Link; Lung diseases; Maintenance; Mediating; Methodology; Mining; Outcome; Pathway interactions; Patients; Pharmaceutical Preparations; Primary Ciliary Dyskinesias; Privatization; Proliferating; 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; design; drug induced liver injury; flexibility; graph knowledge base; hackathon; high throughput technology; improved; innovation; knowledge graph; knowledge integration; 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