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(http://www.niehs.nih.gov//portfolio/index.cfm?do=portfolio.grantdetail&&grant_number=U24ES035214&format=word)
| Principal Investigator: Tropsha, Alexander | |
|---|---|
| Institute Receiving Award | Univ Of North Carolina Chapel Hill |
| Location | Chapel Hill, NC |
| Grant Number | U24ES035214 |
| Funding Organization | National Institute of Environmental Health Sciences |
| Award Funding Period | 05 Sep 2022 to 30 Jun 2027 |
| DESCRIPTION (provided by applicant): | 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. |
| Science Code(s)/Area of Science(s) |
Primary: 81 - Statistics/Statistical Methods/Development Secondary: 03 - Carcinogenesis/Cell Transformation |
| Publications | See publications associated with this Grant. |
| Program Officer | Christopher Duncan |